Seasonal changes and the interaction between the horse chestnut leaf miner Cameraria ohridella and the horse chestnut leaf blotch Guignardia aesculi

: The horse chestnut leaf miner Cameraria ohridella (Lepidoptera: Gracillariidae) is an invasive pest of horse chestnut and has spread through Europe since 1985. The horse chestnut leaf blotch Guignardia aesculi (Botryosphaeriales: Botryosphaeriaceae) is a fungal disease that also se-riously damages horse chestnut trees in Europe. The interaction between the leaf miner and the fungus has not yet been sufficiently described. Therefore, the aim of the present study was to assess leaf damage inflicted to horse chestnut by both C. ohridella and G. aesculi during the vegetation season and to model their interaction. The damage to leaf area was measured monthly from May to September 2013 in České Budějovice , the Czech Republic. A simple phenomenological model describing the expected dynamics of the two species was developed. The study revealed a significant effect of sampling site and sampling period on the damage caused by both the pest and the fungus. The mathematical model indicates that infestation by C. ohridella is more affected by G. aesculi than vice versa . The possible mechanisms affecting the relationship between G. aesculi and C. ohridella are discussed.


Introduction
The horse chestnut tree Aesculus hippocastanum L. (Sapindales: Sapindaceae) has been planted as an ornamental tree in Europe since the 17 th century [1]. Horse chestnut trees are frequently grown in city parks where they provide shade and reduce the urban heat island effect. Aesculus hippocastanum is also an important source of pollen and nectar for pollinating insects [2] and habitat for many organisms including natural enemies, e.g. phytoseiid predatory mites [3][4][5].
The tree is attacked by an invasive pest, the horse chestnut leaf miner Cameraria ohridella Deschka & Dimic (Lepidoptera: Gracillariidae) which was described for the first time in 1985 near Lake Ohrid where it occurred in the area between Macedonia and Albania [6]. The moth spread to all countries of Europe in less than three decades. In the Czech Republic, it was first recorded in 1993. Since 2004, horse chestnut leaf miner has also spread in Asia Minor [7][8][9]. Though the main host tree is horse chestnut, the leaf miner occasionally attacks sycamore maple (Acer pseudoplatanus L.) [10]. Cameraria ohridella usually has three to four overlapping generations during the whole vegetation period. The number of generations per year depends on the temperature conditions [11,12]. The feeding activity of C. ohridella larvae causes the accumulation of phenolic compounds in tissues located at the border of mines, the desiccation of the adjacent leaf parts and the death of the epidermis on both sides of the leaf [13,14]. Heavily infested was 23-37 years in Třebotovice and Kaliště village and 31-56 years in Stromovka Park. Only the Stromovka Park site is situated near a wetland, providing suitable conditions for the development of G. aesculi. All horse chestnut trees grow within 12-29 m of the wetland borders. Two rivers and some streams flow through České Budějovice, as seen from the map. Previous studies [26,35] have demonstrated higher damage to leaves attacked by G. aesculi in the city center than in other areas. At the Nádražní Street site, leaf litter is not removed; therefore, A. hippocastanum leaves are severely infested with C. ohridella every year [35]. The average cadastral area per site was 107.25 ha (SEM = 35.44, n = 8), and the average of green public open space was 21.55 ha (SEM = 7.43, n = 8). The average number of horse chestnut trees was 50.13 (SEM = 20.58, n = 8), and the average age of the studied trees was 52.13 years (SEM = 7.60, n = 8) at the study sites.

Sampling of horse chestnut leaves
Leaf samples were collected at the eight sites five times during the whole vegetation season. The leaves from one site were collected on the same day. Sampling did not take place within 48 hours after a rain event. The sampling dates were from 16 to 24 May, from 13 to 21 June, from 11 to 19 July, from 11 to 22 August and from 9 to 19 September. The standard sample of leaves from a site contained thirty randomly selected compound leaves from individual tree branches up to 2.5 m aboveground. The leaf stalk was only used to handle the leaf. After removal, individual leaves were put into a plastic bag and immediately placed in a portable cool box to be transported to the laboratory, where the leaves were stored in a refrigerator at 4°C for a short time.

Measurement of proportion of leaf area damaged by leaf miner and fungus
Total leaf area and damaged area of the sampled leaves were measured by image analysis of digital photographs. First, the leaves were photographed in a temporary photo studio. Each compound leaf was laid on a white semimatte board (100×100 cm) with a measurement scale and grayscale calibration chart to be photographed by a Nikon D 5100 equipped with a lens Nikon AF-S NIKKOR 18-55 mm, 1: 3.5-5.6 G (Nikon Corporation, Tokyo, Japan). The camera was fastened vertically to a tripod placed 75 cm above the leaf. The leaf was lit by three halogen lamps (400 W each). One lamp was placed one meter behind the board and illuminated the backside of the leaf; two lamps were placed at an angle of approximately 45 degrees above the semimatte board at a distance of 1.5 m and illuminated from the side of the leaf. It was necessary to limit other light sources to a minimum, and care was taken to fasten the camera in a stable position and maintain the same setting while taking photos of the whole series. Leaflets of a compound leaf were fixed so that they did not overlap. This enabled us to analyze images semiautomatically by our program.
Pictures were taken at a high resolution of 3264×4928 pixels and saved in JPEG format (the average file size was approximately 2 MB). The images were digitally cleaned using Adobe Photoshop CS 3 (Adobe Inc., San Jose, California). The main principle of analysis was based on thresholds; therefore, the contrast between the green leaf area and white background was first increased. Then, with the purpose of measuring the total leaf area, the leaf stalk and all possible impurities on the white background were painted with a white color. In order to measure leaf area damaged by C. ohridella, the area of leaf mines was repainted with a white color. This picture was saved before using it to assess the leaf area damaged by G. aesculi. The area destroyed by this fungus was repainted, and the resulting image saved separately. Marking the area and repainting was performed manually by means of 3D SpaceNavigator TM model 3DX-700028 (3Dconnexion, Boston, Massachusetts, USA) and standard tools in Adobe Photoshop. Each sampled leaf thus resulted in three images amounting to 1,200 threshold images during the entire study. These images were subsequently processed by our custom-made software written in Ja-va™ programming language [36], which saved the values of the areas (number of black pixels in an image) in CSV files (comma-separated values). The damages attributed to C. ohridella and G. aesculi were finally calculated as percentages of the total leaf area.

Data presentation and statistical analysis
All measured data stored in CSV files were imported into a Microsoft Access 2010 database. Because the dependent variable (percentage area destroyed) was not normally distributed, the arcsine square-root transformation [37,38] was used to normalize data before the statistical analysis. Two-way multivariate analysis of variance (MANOVA) with sampling site and period of sampling as the main factors was used to analyze the data. The analysis was performed using STATISTICA v. 13.2 software (StatSoft Inc., Tulsa, OK, USA).

Modeling dynamics of leaf damage caused by Cameraria ohridella and Guignardia aesculi
Since the interactions between C. ohridella and G. aesculi during a vegetation season may change as the proportions of damaged leaf area attributed to each species increase, we developed a simple phenomenological model describing the expected dynamics of the two species. Because new leaves were sampled at each sampling occasion, we did not have sufficient information to validate the model against data obtained from individual leaves. It was therefore assumed that the processes taking place at individual leaves are reflected by the data obtained as average proportions of damage per sampling site assessed during the five sampling periods.

The model
The proportions of leaf area damaged by C. ohridella and G. aesculi at time t are denoted pC(t) and pG(t), respectively, where 0 ≤ pC(t) ≤ 1 and 0 ≤ pG(t) ≤ 1. Furthermore, as the total damaged leaf area at time t, found as ptotal (t) = pC(t) +pG(t), cannot exceed 1, we also have the constraint that 0 ≤ ptotal(t) ≤ 1.
Since injured leaves do not recover from damage, pC(t) and pG(t) may increase monotonically with time during a season, but not necessarily at the same speed. The speed approaches 0 as ptotal approaches 1. The rate of increase in the proportion of damaged area caused by the i'th species is modelled by the generalized logistic equation where αi, i and γi are species-specific parameters. When the parameters of Eqn 1 have been estimated (see below), we can use the model to estimate the mutual effect of the two species on each other by comparing the damage each species inflicts when it is either alone or together with the other species. The relative impact imposed by the competitor species on species i at time t is found as where pi(t) is the damage caused by species i when it coexists with its competitor, while ′ ( ) is the predicted damage when it occurs alone, i.e. by setting ptotal = pi in Eqn 1.

Estimation of model parameter values
The predicted values were obtained from Eqn 1 by replacing dpi/dt with Δpi/Δt and then iterating the model with a time step (Δt) of a half day from time t = 0. Thus, the predicted damage attributed to species i at time t+Δt was calculated as ̂( + ∆ ) = ̂( ) + ∆̂. t was calculated as the number of days since the first sampling. Because sampling was conducted over several days, we used the midpoints of each sampling period to calculate t, implying that t = 0 corresponds to May 20. Thus, for the following four sampling periods t was set to 28, 56, 85.5, and 117 days, respectively.
We assumed that the underlying biological processes taking place at the eight sampling sites were the same, implying that the values of the model's six parameters were independent of sites, in contrast to the values of pi(0), which were estimated for each site and species. We used the Solver tool in Excel ® to estimate the 22 unknown constants of the model (6 parameters and 16 site-specific values of pi(0)) as the values that minimized the sum of squared deviations between the observed and estimated values of pi(t). The quality of a fit was expressed as the proportion of total variation explained by the model (R 2 ) while the significance of the R 2 was assessed by an F-test as the Mean Square (MS) of the model divided by the Mean Square Error (MSE) with p-1 degrees of freedom (df) in the numerator and n -p df in the denominator. p is the number of estimated values (22) and n the number of data points (80).
The parameter values estimated above were also used to model the progress in overall damage (average of the eight sampling sites). This reduced the number of constants to 2 (i.e. one estimate of p(0) per species), which were estimated by fitting the model to 10 data points. The resulting model was used to evaluate the relative impact the two pest organisms have on each other.

Evaluating the model as a predictive tool
We also examined whether the model could serve as a tool for early warnings based on data sampled in the beginning of the season. Since the mathematical model is deterministic and sensitive to the initial state of the system, two conditions have to be satisfied in order to use the model for tactical purposes: (i) The sample-based estimates of pi(0) (denoted ̅ ) should be close to the a posteriori values (denoted ̂), obtained retrospectively by fitting the model to data spanning over the entire season; and (ii) the biological system should basically be predictable with relatively little noise obscuring the underlying dynamics described by the model.
The first condition was tested by means of linear regression, where values of ̅ were regressed against the corresponding values of ̂, yielding eight data points per species. The condition was met if the line showed a significant positive correlation with a slope close to 1 and an intercept close to 0 so that ̅ = ̂. The second condition was tested by calculating the correlation coefficients between the damage assessed at sampling k (k = 1,2,3,4) and the final damage assessed at the fifth sampling, using pairwise data from the eight sampling sites and for each species separately. The condition was met if the correlation coefficient (r) between damages assessed at two sampling occasions was significantly greater than 0. Changes in the average percentage of damage and the range of damaged leaf area inflicted by C. ohridella and G. aesculi across all sites during the vegetation season indicated a cumulative pattern ( Table 2). The average damaged leaf area varied highly among the study sites ( Figure 1, data points). The overall average percentage of leaf area destroyed by C. ohridella and G. aesculi was 3.06% (SEM = 0.23, n = 1200) and 2.75% (SEM = 0.21, n = 1200), respectively. Of the sites, Nádražní Street had the highest leaf damage inflicted by C. ohridella, reaching 78.79%. The highest leaf damage inflicted by fungal pathogens was recorded at the Stromovka Park site and reached 78.86%. Both values were recorded in September. The MANOVA revealed a highly significant effect of both site (Wilk's λ = 0.428, F14,2318 = 87.532, P < 0.0001) and sampling period (Wilk's λ = 0.199, F8,2318 = 359.882, P < 0.0001) on leaf damage. The interaction between the site and period was also highly significant (Wilk's λ = 0.629, F56,2318 = 10.813, P < 0.0001).

Model of leaf damage caused by Cameraria ohridella and Guignardia aesculi
The parameter values of the model reveal that the damage due to C. ohridella is expected to increase more steeply than that of G. aesculi when both species occur at low abundances in the beginning of the season. On the other hand, C. ohridella tends to be more inhibited by damage inflicted by both itself and its competitor than G. aesculi is. Thus, the model predicts that damage due to C. ohridella, even in absence of G. aesculi, will level off and not exceed 50%. In contrast, G. aesculi seems rather unaffected by the presence of C. ohridella and may cause close to 100% damage if the season is long enough. As seen from Figure 3, the relative impact of G. aesculi on C. ohridella reaches more than 70% while that of C. ohridella on G. aesculi is only about 10%.

The model as a predictive tool
Though the model in retrospect provided a good fit to seasonal data, its capacity as a tactical tool for long-term forecasts of leaf damage seems limited by the fact that there was no agreement between values of ̂ and ̅ (C. ohridella: ̅ = 0. This finding is confirmed by the fact that damages assessed from the first sampling (mid-May) were poorly correlated with the final damages assessed in mid-September ( Table 4). The same applies to data collected in mid-June, whereas data collected in mid-July and mid-August were more strongly correlated with the final damages.

Damage to the leaf area of A. hippocastanum during the vegetation period
The horse chestnut leaf miner was first recorded in the Czech Republic in 1993 and in the course of five years, it spread across the whole country [7,39]. The fungal disease G. aesculi probably spread within the Czech Republic in the 1950s [18]. Both species commonly attack horse chestnut trees in the country [18,26,35].
Regarding C. ohridella, very little damage was found in the first sampling period in May, while one month later, the amount of damage was ten times higher. This finding is consistent with the results of a previous study that reported that the first generation of horse chestnut leaf miners started to hatch at the end of April [35]. The diapausing pupas Date of C. ohridella overwinter on fallen leaves. Therefore, the highest population density of horse chestnut leaf miner is expected at sites where no raking occurs [27,40]. This was the case for the Nádražní Street site, where damage reached the highest level compared to the other sites in mid-July, and this trend persisted for the remainder of the vegetation season. The study by Salleo et al. [17] revealed that serious damage to leaf area causes premature tree defoliation. The combined damage to the leaves exceeded 50% at the no-raking sites in August. The last sampling in September revealed less damage, which might be because the most damaged leaves had already fallen off. The first stage of G. aesculi, characterized by water-soaked irregular areas on the epidermis layer of compound leaves, had been recorded in Slovakia from the beginning of April, followed by a rapid spread of this symptom [25]. The occurrence of the first symptoms in April was also indicated by our study because the necrotic leaf area of horse chestnut was observed in mid-May. The spermogonia stage of G. aesculi overwinters on fallen leaves of horse chestnut; therefore, the Nádražní Street site should have been the most infected with G. aesculi [18,22]. However, the increased incidence of leaves infected by G. aesculi at this site in spring was not obvious. Our previous study [26] indicated that high air humidity promotes leaf damage caused by G. aesculi. This finding was also confirmed in the present study. In August, the greatest leaf area damaged by G. aesculi was recorded at three sites: the city center, where many trees grow in parks along two rivers; the Nádražní Street site that was not mowed, which might have contributed to an increase in air humidity; and the Stromovka Park site, where the trees grow near wetlands. In September, Stromovka Park began to have the greatest damaged leaf area due to G. aesculi, and this trend remained until the end of the vegetation period. The average damaged leaf area caused by G. aesculi was found to be 3.9% in Rimavská Sobota Park in Slovakia in September [25]. In comparison, the average leaf area damaged by G. aesculi found in the present study was 9.6%.

Interaction between C. ohridella and G. aesculi on the leaves of horse chestnut trees
According to Hatcher [41], the relationship between herbivorous insects and fungal diseases is often competitive, as they share the same resource. Survey conducted in Bern in September by Gilbert et al. [30] reported that the occurrence of horse chestnut leaf blotch estimated on a score between 0 (not present) and 4 (very high) was inversely related to infestation by horse chestnut leaf miner. The mechanisms of interaction between these two species have not yet been fully elucidated.
The model presented in this study aimed at explaining the underlying dynamics of two pest organisms (C. ohridella and G. aesculi) simultaneously attacking leaves of horse chestnut trees. The model has only two state variables, representing the current proportions of leaf area damaged by each species, and three parameters per species (denoted α, β and γ). α represents the relative damage rate (increase in proportional damage per day), β expresses whether the damage rate depends on how much area the species has already destroyed, and γ expresses how fast the damage rate declines as the total damage progresses. As seen from Table 3, α for C. ohridella was estimated to be more than 60 times higher than for G. aesculi, indicating that the former species has the potential to become a serious pest. On the other hand, γ for G. aesculi was found to be much smaller than that of C. ohridella, indicating that G. aesculi can achieve higher levels of damage than C. ohridella. Both species have values of β values close to unity, which indicates that damage rate is speeded up the more damage a species has already inflicted. This seems reasonable for G. aesculi since the production of spores is likely to increase proportionally with the infected area. For C. ohridella, the explanation for the positive β is likely to be a combination of recruitment of young larvae, an increasing number of mines due to overlapping generations [11,12], and a positive feedback between the body size and the feeding rate of individual larvae. As the larvae grow, their larger size will speed up their total damage rate even if the number of larvae per leaf declines due to pupation and mortality. When the larvae reach their final body size they stop feeding and pupate. Studies have shown that the proportion of pupae entering diapause is gradually increasing during high-summer [12,42], which may explain why the increase in observed damage ceases from around mid-August. In contrast, G. aesculi continues to destroy leaf area throughout the entire vegetation season (Fig. 1 and 2). Figure 2 shows that C. ohridella is expected to reach higher levels of damage in absence of G. aesculi compared to when the fungus is present, whereas the effect of C. ohridella on G. aesculi is rather small (Figure 3). This result contrasts with the findings of a previous study [32] where the authors measured the response of horse chestnut saplings to separated and simultaneous colonization by C. ohridella and G. aesculi. The authors revealed that leaf damage increased faster when only the fungal pathogen attacked the plants than when they were infected by both pests. The authors concluded that simultaneous infestation by fungal and insect agents made conditions unfavorable for the former species. The strong influence of G. aesculi on C. ohridella found in the present paper is attributed to the circumstance that feeding of C. ohridella larvae is hampered by lack of suitable food, which may simultanously decrease the per capita feeding rate and increase the larval mortality. In contrast, G. aesculi is only limited by the amount of undamaged leaf area. However, resource competition may not be the only explanation for why C. ohridella is more sensitive to the presence of G. aesculi than vice versa. Thus, it cannot be excluded that interference competition may also play a role, e.g., if the fungus produces substances that are toxic or repellent to C. ohridella. Content of fenolic compounds, secondary metabolites produced by plant during defence response to pest or pathogen attack, was found to be higher in A. hippocastanum saplings on which both insect and leaf blotch disease were co-existing compared to a single-species infestation [32]. An evidence of a repellent role of volatile organic compounds (VOCs), which were found to be emitted by horse chestnut leaves in response to fungal infections by G. aesculi and/or powdery mildew Erysiphe flexuosa (Peck) Braun and Takam was given by Johne et al. [34]. In contrast to these results, experiments by Jagiełło et al. [33] did not confirm that females of C. ohridella deposit eggs more frequently on healthy leaflets than on infected ones. A recent study demonstrated that the phyllosphere of A. hippocastanum is inhabited by many generalist endophytes, epiphytes and saprotrophic fungi but that occurrences of common phyllosphere fungi were unrelated to the degree of damage caused by C. ohridella [43]. A positive interaction was, on the other hand, described between horse chestnut leaf miner and bleeding canker of horse chestnut (Pseudomonas syringae pv. aesculi) which is a serious bacterial disease that is lethal to A. hippocastanum, unlike C. ohridella and G. aesculi. In this case, feeding by C. ohridella larvae may cause suppression of two defensive enzymes within wood tissue and thus facilitates bacterial infection [44,45]. We think that data based on repeated observations of the same leaves throughout an entire season could help deciding to what extent chemical defense mechanisms are involved in the competitive interactions, for instance by studying whether C. ohridella establishes more readily on leaves free of G. aesculi than on leaves already infested by leaf blotch.
We investigated to what extent the strategic model presented in this paper can also be used as a predictive tool to forecast whether a stand of chestnut trees is in risk of suffering severe damage later in the season. Having a reliable forecast system would assist in deciding when and where treatment with pesticides will be needed. However, a crucial factor for making accurate predictions is that the current state of the system can be determined with little uncertainty and then used as input in a model that predicts the system's state later in the season. Sampling error in itself may not be a serious problem as it can be solved by increasing the number of leaves sampled. A more serious problem is that the inherent predictability of the system seems to be low. Thus, we did not find evidence of a correlation between the damage assessed in mid-May and the damage assessed in mid-September, rendering long-term prognoses based on modeling unlikely. This is not surprising in view of the multitude of factors that may influence the relationship, but not incorporated in the model, such a temperature, precipitation, humidity, dispersal, natural enemies etc. [46]. All these unknown factors add up as "noise", which is likely to override the underlying deterministic signal represented by the present model. To incorporate external factors in a predictive model would increase its complexity far beyond the one presented in this paper and may require a model that can cope with stochasticity in order to generate predictions in terms of probabilities rather than expectancies. On the other hand, since the level of damage assessed in mid-July was found to be a good predictor of final damage, decisions as to whether control measures should be implemented or not could be taken in mid-July at earliest.

Conclusions
This study investigated damage inflicted to horse chestnut leaves by a leaf miner (C. ohridella) and a fungal pathogen (G. aesculi). The obtained results revealed that: (1) The damage caused by both pests varied significantly among sampling sites within the city, (2) The overall leaf damage exceeded 50% in no-raking sites in August, (3) The model indicates that G. aesculi had high impact on C. ohridella, whereas the impact of C. ohridella on G. aesculi was small, (4) Though the model fitted data very well, its predictive power is too low to constitute a reliable management tool.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.