Diurnal patterns in dispersal of Monilinia fructigena conidia in an apple orchard in relation to weather factors

agricultural and forest meteorology 149 (2009) 518–525

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Diurnal patterns in dispersal of Monilinia fructigena conidia in an apple orchard in relation to weather factors§ Finian Bannon a, Gerrit Gort b, Gerard van Leeuwen c, Imre Holb d, Mike Jeger e,* a

Northern Ireland Cancer Registry, Queens University of Belfast, Mulhouse Building, Grosvenor Road, Belfast BT12 6BJ, United Kingdom Biometris, P.O. Box 100, 6700 AC Wageningen, The Netherlands c Plant Protection Service, P.O. Box 9102, 6700 HC Wageningen, The Netherlands d Centre of Agricultural Sciences, University of Debrecen, P.O. Box 36, H-4015 Debrecen, Hungary e Division of Biology, Imperial College London, Silwood Park, Ascot SL5 7PY, United Kingdom b

article info

abstract

Article history:

The aerial concentration of Monilinia fructigena (causing brown rot in pome fruit) conidia was

Received 1 August 2007

sampled during 1997 and 1998 in an apple orchard and was related to weather conditions.

Received in revised form

The highest hourly concentration measured in 1997 was 230 conidia/m3, in 1998 concen-

23 September 2008

trations were lower than in 1997 throughout the season. In both years concentrations were

Accepted 1 October 2008

highest in the afternoon hours. Generalised linear models, employing a Poisson distribution for the spore counts and a logarithm link function, were used to study the relationships between spore counts and lagged weather variables. In 1997 the best fitting model had

Keywords:

variables temperature lagged at 100 h, an east–west component of wind direction, and wind

Aerial dispersal

speed; whereas in 1998 the best model included in addition an effect of relative humidity.

Brown rot fungi

Temperature and wind direction had consistent effects on hourly spore counts in both

Epidemiology

years, but whereas temperature has a biologically relevant effect on spore production and

Generalised linear model

maturation, the effect of wind direction is likely to reflect the purely local effect of orchard layout. Results are compared with observations made in stone fruit orchards where Monilinia laxa and Monilinia fructicola are the predominant species, and differences in epidemiology between these systems are discussed. # 2008 Elsevier B.V. All rights reserved.

1.

Introduction

The brown rot fungi of fruit crops (Monilinia spp.) cause blossom blight and fruit rot in rosaceous fruit crops in the temperate regions of the world (Byrde and Willetts, 1977). The group consists of three species: Monilinia fructicola, Monilinia laxa and Monilinia fructigena. Recently a new anamorphic species Monilinia polystroma has been designated (Van Leeuwen et al., 2002a). M. fructicola is listed as a quarantine pest for Europe (Van Leeuwen et al., 2001). Wind, water, insects, birds and man are responsible for the dispersal of §

Monilinia conidia in pome and stone fruit orchards (Byrde and Willetts, 1977). Pauvert et al. (1969) found that splash dispersal is important for short range spread within a tree, while Lack (1989) reported spread by insects, and Kable (1965) discovered that airborne conidia ensured a wide dispersal of conidia within an orchard. The viability of dispersed conidia has on occasion been studied (Xu et al., 2001a; Holb, 2008). Van Leeuwen et al. (2002b) observed that late infected fruits in one season can contribute to primary inoculum of M. fructigena in the next spring and in early summer dropped fruit can contribute to infection on the tree (Holb and Scherm, 2007).

This research was supported by a British Society of Plant Pathology Fellowship and by Enterprise Ireland’s International Collaboration Programme. * Corresponding author. Tel.: +44 207 594 2719; fax: +44 207 594 2601. E-mail address: [email protected] (M. Jeger). 0168-1923/$ – see front matter # 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2008.10.001

agricultural and forest meteorology 149 (2009) 518–525

Thinned fruit can provide inoculum of M. fructicola during a season (Hong et al., 1997). Airborne dispersal of fungus spores is an important component of epidemiology and a major consideration in devising disease management strategies (Aylor, 1999). Dispersal can be monitored directly through spore sampling techniques or in some cases indirectly through infection gradients. Most of the work on aerial dispersal of Monilinia conidia has been done with M. fructicola in stone fruits (Kable, 1965; Jenkins, 1965; Luo et al., 2007). A major reason for studying the spatial spread of M. fructicola has been the tracking of fungicide resistant strains (Zehr et al., 1991; Elmer et al., 1998). In apricot orchards infected with M. laxa, Corbin et al. (1968) found that the aerial spore content rapidly increased 10–14 days before the harvest ripe stage. In pome fruits, M. fructigena is the predominant species causing fruit rot, and dispersal of conidia by water and insects has been studied (Pauvert et al., 1969; Lack, 1989). Airborne conidia have been trapped on exposed dishes and vaseline slides (Horne, 1933; Bucksteeg, 1939). Recently the aerial density of M. fructigena conidia was reported to increase continuously from the appearance of first infected fruit to harvest (Holb, 2008). The spatio-temporal dynamics of disease development have been studied (Van Leeuwen et al., 2000; Xu et al., 2001b). In addition to seasonal patterns, distinct diurnal patterns in aerial spore content have been observed and related to environmental conditions. Peak concentrations of Monilinia conidia occurred during the afternoon when relatively low air humidities and high wind speeds prevailed (Kable, 1965; Corbin et al., 1968). Spore production is often triggered by light which may impose a diurnal pattern under natural day length conditions. Results with Monilinia have been equivocal when investigated in vitro with small and inconsistent differences between continuous darkness and light/dark cycles (Van Leeuwen and van Kesteren, 1998; Sanderson and Jeffers, 2001). Relationships between environmental factors and aerial spore concentration have been studied extensively, but in most cases analysis has been restricted to simple regression analysis (Corbin et al., 1968; Sutton and Jones, 1979; Sanderson and Jeffers, 1992; Holb, 2008). Most workers have related aerial spore concentration with contemporaneous environmental conditions, however for some variables a certain lag period may be more appropriate. Byrde and Willetts (1977) discovered that dehiscence of conidial chains in Monilinia is stimulated by a decrease in the ambient relative humidity (RH), but dehiscence and final capture of conidia may be separated in time. Fluctuations in airborne numbers of M. laxa conidia in an apricot orchard in 1966 were reported by Corbin et al. (1968). Spore concentrations were correlated with temperature, RH, and wind. During the daytime hours until about 15.00 h, temperature and wind speed increased, while RH decreased; thereafter temperature declined, RH increased, but wind speed continued to increase until 17.00. Airborne numbers of conidia continued to increase until 18.30 and it was concluded that wind speed was probably the most important weather variable. Sanderson and Jeffers (1992) related spore trap data of Monilinia oxycocci to a number of weather variables. The diurnal pattern of spore release coincided with increasing temperature and wind speed, and decreasing RH. Rainfall

519

amount reduced spore catch on a number of specific days. The significance of correlation coefficients between spore catches and weather variables was not determined because of autocorrelation among hourly summaries of the variables and intercorrelation among different variables. The spore trap data were presented as hourly spore counts with many zero values at night. Holb (2008) correlated aerial spore density of M. fructigena with environmental variables. Temperature and relative humidity correlated best with mean hourly conidial counts; whereas correlations with wind speeds and mean hourly rainfall were inconsistent in different orchards or poor, respectively. The selected examples highlight a number of problems with spore count data. Firstly, under certain circumstances, the count data may be low and contain many zero values. For such count data, the assumption of a normal distribution as representing the underlying distribution of residuals is incorrect, thereby by invalidating the use of ordinary regression (Draper and Smith, 1998). Secondly the situation described above may be avoided by extending the observation period. This will decrease the frequency of zero spore counts but may obscure real effects as weather variables may influence spore release on a finer time scale, as evidenced by diurnal patterns. Thirdly, when using ordinary regression to model spore count data, the best fitting model may predict negative values for spore counts at the extreme ends of the explanatory variable’s range. In this paper we present a 2-year study of the aerial content of M. fructigena conidia in an apple orchard during the growing season. We analyse time series of count data in relation to weather variables, with possibly lagged effects of some variables used as regressors as done in other epidemiological and ecological studies (Welty and Zeger, 2005; Hu et al., 2006; Sims et al., 2007).

2.

Materials and methods

The study was conducted in an experimental orchard situated in Kesteren in the Rhine basin in the centre of the Netherlands during 1997 and 1998 (Van Leeuwen et al., 2000; Fig. 3). A Burkard 7-day recording volumetric spore trap (Burkard Manufacturing Co., Rickmansworth, Hertfordshire, UK) was placed in the centre of a plot which consisted of three rows of the apple cultivar James Grieve alternating with two rows of cultivar Cox’s Orange Pippin, both planted in 1984. The plot was flanked by blocks with apple cultivars Golden Delicious and Lombarts Calville at the northwest side, and old bush apple trees at the southwest side. The remaining sides were flanked by high windbreaks. The spore trap was placed in the middle of a row where a tree was missing, and operated continuously with the orifice 2 m above ground level from the beginning of May until mid-October in 1997, and from mid-July until mid-September in 1998. In both years, the spore trap was operated following standard procedures (Lacey and Venette, 1995; Willocquet and Clerjeau, 1998) as set out in the Burkard operating manual. Conidia were trapped on a strip of Melinex tape wrapped around a rotating drum, changed every 7 days and marked at distances of 200 mm, corresponding to hourly time intervals.

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M. fructigena conidia vary in size and shape, ranging from elongate-ellipsoid to ovoid and limoniform. Only limoniformshaped conidia with a length greater than 20 mm could be identified definitely as M. fructigena conidia (Van Leeuwen and van Kesteren, 1998) and therefore only these were counted. As we only counted a sub-group of the total spores released into the atmosphere, the [absolute] spore concentration was underestimated. However, the relative differences between spore counts at different weather conditions would be unbiased if the proportion of the counted spore size among all the released spores remained constant across weather conditions. Hourly spore counts were made during the main dispersal period from mid-July until the end of August in 1997 and 1998.Hourly records of meteorological data were obtained from a weather station, located approximately 15 m from the spore trap. Temperature (8C) was measured in a Stevenson screen at 1.5 m height, wind speed (m s 1) and wind direction with a 100r switching anemometer, and rainfall (mm) with a standard pluviometer. All variables were recorded every 30 min. RH (%), recorded on an hourly basis, was obtained from a hygrometer in a weather station 10 km from the orchard as the hygrometer inside the orchard did not function properly during a part of both years; RH data from the weather station showed a strong correlation with those from inside the orchard (r = 0.96). Wind direction was measured in degrees with 3608 due north. The period from 30 July to 10 August (12 days) in both 1997 and 1998 had high spore counts and no missing values for the spore counts and the meteorological variables (and lags that were tested). This ‘study period’ represents the data analysed. Only whole days were used: the 24 h of the same 12 days in each calendar year were used, 288 in all. The data sets obtained for 1997 and 1998, covering the study period, are available at http://www8.imperial.ac.uk/ content/dav/ad/workspaces/naturalsciences/people/m.jeger/ files.zip. A generalised linear model (GLM) was constructed to relate the conidial counts to the environmental variables, or lags of them. The GLM approach is characterized by three components: (1) a response variable with a probability distribution that is a member of the exponential family of distributions; (2) a linear combination of explanatory variables with associated parameters; (3) a link function that determines how the expected value of the response variable will depend on the linear combination of the explanatory variables (Dobson, 1990). The parameter values corresponding to explanatory variables were estimated by maximum likelihood. The adequacy of a model was evaluated by calculating the deviance (D) of the model, where D follows a x2N p distribution with N the number of observations and p the number of parameters in the model. The Poisson distribution was used to describe the distribution of count data, with a logarithmic link function to the explanatory variables. The data analysis involved two parts: (1) a screening phase, that identified lagged explanatory variables that were consistently present in the models with low deviance; and (2) a model-building phase, that combined these identified variables, by forward selection, into a multivariate model. In the screening phase, the potential number of lags for each explanatory variable was restricted based on a priori

assumptions derived from the literature. For relative humidity (RH), wind speed (WS) and wind direction (WD), it was assumed that these variables would influence mainly spore release. Consequently, lags of up to 20 h were considered as RH affects dehiscence of conidial chains, and WS and WD may affect the spore load in the air next to the trap. For temperature (TP), lags of up to 180 h were considered because of its possible effect on conidial development. Rain (RN) was infrequent in the study periods analysed (Figs. 2f and 3f), and was omitted from the screening phase, but was later incorporated in the forward selection of model-building. To simplify screening WD (0–3608) was converted into a qualitative variable (WDQ) with four levels corresponding to the quadrants of a wind rose. The screening phase involved systematically regressing, using GLM as described above, spore counts against all possible combinations of the explanatory variables for lags of TP from 0 to 180 in steps of 10 h and of RH, WS and WD from 0 to 20 in steps of 5 h: a total of 2375 combinations. Each weather variable entered the regression with a linear and quadratic term. Lags of environmental variables that were consistently present in models with a low deviance across both years were chosen for the model-building phase. In this phase the lagged explanatory variables were added by forward selection to construct a multivariate model. Linear, quadratic, and interaction terms were retained in the model only if they lowered D significantly (x2-test, P < 0.05). In observational studies, there may be covariance between independent variables (e.g. weather variables in this study). If two weather variables are highly correlated, and basically describe the same variation in the dependent variable (spores in this case) then in the model they are substitutable for one another. It is not possible to assign causation to one over the other, and indeed neither may be variables in a causation pathway. With highly correlated variables that were substitutable, preference was given to those whose coefficients were similar in magnitude and sign between years. In addition, if a variable, when entered last in the model (Type III analysis), failed to reduce deviance, it was omitted—the other variables being deemed sufficient. Wind direction was broken down into trigonometric components with NS a variable measuring the north–south and EW measuring the east–west component of the WD vector. The data sets formed part of a time series, so it was necessary to check serial correlation among the residuals. If parameter coefficients are not biased by serial correlation then this is an asset for comparing the 2 years of data, the coefficients can be compared for the same variables, irrespective of whether they are significant in both years. If the coefficients are similar for the same variable, then this may point towards a real relationship, though not necessarily a causative one.

3.

Results

From May until mid-July in 1997 and after mid-September, M. fructigena conidia were trapped only occasionally. The first fruits bearing pustules of M. fructigena were detected in trees of cv. James Grieve in the first week of July, and subsequently aerial conidia were detected regularly. A peak in daily spore

agricultural and forest meteorology 149 (2009) 518–525

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concentration (conidia/m3) was found in the beginning of August in 1997, after a rainy period and with a period of hot, dry weather. In 1998, aerial spore concentrations were much lower, and no peak was observed. The highest hourly concentration recorded was 233 conidia/m3 in 1997, and 83 conidia/m3 in 1998, both occurring in the afternoon hours. The frequency of occurrence of M. fructigena conidia was highest during the afternoon hours, though in 1997 conidia were also detected in the early morning (04.00–05.00) in almost 50% of the days. Mean spore counts by hour of day (a) and the associated diurnal profiles for weather variables (b–f) over the study period are shown in Figs. 1 (1997) and 2 (1998). Mean hourly spore counts were higher in 1997 than 1998. In general, spore counts were higher between 12.00 and 18.00 than in the 6-h periods either side of this interval. Temperature was generally higher in 1997 than 1998 although the daily range (max–min) was greater in 1998 (Figs. 1 and 2b). Temperature followed a consistent diurnal pattern with the mean daily temperature steadily increasing throughout the study period. Relative humidity also followed a diurnal pattern, the inverse of that for TP (Figs. 1 and 2c). Windspeed patterns were broadly similar in both years (Figs. 2 and 3d). There was a diurnal pattern in windspeed, though this was more erratic than for either TP or RH. In 1997, wind direction was mainly from the south–east initially, and then from the north for the remainder Fig. 2 – (a) Mean hourly conidia counts trapped during the study period in 1998: in relation to (b) mean hourly temperature (8C), (c) relative humidity (%), (d) wind speed (m sS1), (e) wind direction (angles with respect to due north), and (f) rainfall amount (mm hS1).

Fig. 1 – (a) Mean hourly conidia counts trapped during the study period in 1997: in relation to (b) mean hourly temperature (8C), (c) relative humidity (%), (d) wind speed (m sS1), (e) wind direction (angles with respect to due north), and (f) rainfall amount (mm hS1).

of the period (Fig. 2e); in 1998, from 30 July to 1 August wind came from the south east, then the large fluctuations either side of 08 during 1–3 August, suggesting a northern wind direction, with some evidence of a diurnal pattern thereafter (Fig. 3e). Rainfall was restricted to occasional days only during the study period (Figs. 1 and 2f). In 1997, the main features of the screening were that TP at lags of 40, 100, and 140 h in models with low deviance; closer examination of these groups showed that a lag of 15 h in WD further reduced deviance, as did RH lagged by 15 h. Lags of WS did not contribute any discernible reduction in deviance. In 1998, TP at lags 100 and 180 h were present in groups of models with low deviance; within these groups, lags of WD at 0 and 20 h, lags of RH at both 0 and 25 h, and a zero lag in WS produced further reductions in deviance. Considering both years together, it is not surprising that both TP and RH had several lags that produced regressions with similarly low deviance. The diurnal nature of both variables means that there is a high possibility that various lags are equally likely to describe well the variation in spore counts, for example in 1998 RH lagged at 0 and 25 h showed a marked similarity. TP is further complicated as it may be describing more that one effect, i.e. spore dispersal with a short lag and conidial formation with a long lag. The results of the 2 years are valuable as they provide independent evidence of important variables for multivariate modelling.

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Fig. 3 – Correlation between residuals of fitted models and the time lags: (a) 1997 and (b) 1998.

The coincidence of TP100 in both years favoured its selection in multivariate model-building. For the other explanatory variables (RH, WS, WD) that appeared to be influential, but where there were several deviance-reducing lags between and within years, the lowest lag was selected on the assumption that these variables mainly affected spore release and capture. Thus combining these results of the screening and the above a priori assumptions, the following multivariate models were fitted by forward selection to each year: in 1997; TP100 EW0 WS0 WS0*WS0; and in 1998; TP100 EW0 RH0 WS0. Summary statistics for these models are shown in Table 1. In both 1997 and 1998, TP100 was highly significant and had similar coefficients, 0.056 and 0.067, respectively; this meant that for every 1 8C increase in TP 100 h previously, spore counts increased by about 6%. TP100 is probably capturing both a diurnal relationship between temperature and spore counts and some effect of temperature on conidial development 4 days prior to spore catch. The coefficients for EW in 1997 and 1998 were 0.51 and 0.27, respectively; a change in direction of the wind from the east to the west increased the spores caught in the traps, a local effect presumably reflecting aspects of the orchard layout. In 1998 the model gave a

Fig. 4 – Predicted conidia counts over the 24-h period obtained using the best fitting model in relation to the observed mean conidia counts for the study period: (a) 1997 and (b) 1998.

negative coefficient for a linear effect of wind speed; whereas in 1997 there was both a linear and quadratic effect, with a negative quadratic coefficient. Finally RH0 had a significant effect in 1998—for every 1% increase in RH0, spore catches declined by 1.5%. In neither year did RN make a significant contribution to describing spore catches. The residuals produced by the two models are plotted in Fig. 3a and b for 1997 and 1998, respectively. The serial correlation in 1997 is a source of some concern because it may mean that the standard errors of the coefficients in the model are underestimated and the probability of a Type 1 error is increased. In the 1998 data set, there appears to be little evidence of serial correlation and therefore the model fitted is well-estimated. The observed mean hourly spore counts for the 12-day study period are plotted in Fig. 4a and b for 1997 and

Table 1 – Multivariate model fitted to 1997 and 1998 spore catches showing the parameters, degrees of freedom for each parameter (DF), estimates, standard errors of estimate. Parameter

DF

1997 Intercept tp100 ew0 ws0 ws0*ws0

1 1 1 1 1

1998 Intercept tp100 ew0 rh0 ws0

1 1 1 1 1

a b

Standard error

Chi-squarea

0.7439 0.0566 0.5143 0.6524 0.2175

0.2487 0.0108 0.0641 0.2223 0.0906

8.95 27.38 64.4 8.61 5.76

0.0028 <.0001 <.0001 0.0033 0.0164

0.0883 0.0676 0.2676 0.0151 0.2697

0.6335 0.0202 0.101 0.0045 0.0902

0.02 11.18 7.02 11.29 8.94

0.8891 0.0008 0.0081 0.0008 0.0028

Estimate

Chi-square statistic, the test statistic for testing the null hypothesis that the parameter estimate is equal to zero. Pr > Chi = probability of observing a value greater than the observed Chi-square statistic if the null hypothesis is true.

Pr > ChiSqb

agricultural and forest meteorology 149 (2009) 518–525

1998, respectively, together with the average predicted spore counts over the same 24 h. The overall level of spore counts differed between seasons and over the 12 days of the study period the observed mean spore counts were described well by the two models, in two quite different seasons.

4.

Discussion

The aerial spore concentration of M. fructigena increased markedly from the time when the first diseased fruits appeared in the beginning of July, as also found by Holb (2008). In 1997, conidia were trapped only occasionally before diseased fruits appeared, probably originating from sporulating mummies (Van Leeuwen et al., 2002b). Bucksteeg (1939) showed that the aerial concentration of Monilinia spores in a pome fruit orchard peaked during June and July, but this was not related to possible sources. Corbin et al. (1968) found that M. laxa conidia were only detected regularly when approximately 1% of the fruit in the trees was sporulating. The aerial concentration of M. fructigena conidia in the orchard plot was distinctly higher in 1997 compared with 1998. Final disease incidence in cv. James Grieve was equal in both years (Van Leeuwen et al., 2000), although higher in cv. Cox’s Orange Pippin in 1997 compared with 1998 (4.4% and 2.7%, respectively). The maximum hourly concentration detected in our study was in the range 200–250 conidia/m3, lower than concentrations measured in stone fruit orchards. A maximum concentration of 5000 conidia/m3 was measured in a peach orchard with approximately 5% of the fruit infected by M. fructicola (Kable, 1965). A maximum concentration of 1260 conidia/m3 was found in an apricot orchard affected by M. laxa (Corbin et al., 1968). The gradual increase in disease incidence in pome fruits (Van Leeuwen et al., 2000), compared with the rapid increase in stone fruits just prior to the harvest ripe stage (Corbin, 1963; Zehr, 1982), might explain the lower peak concentration observed in our study. The level of contamination of healthy fruits by aerially dispersed conidia would also increase gradually. However, the presence of wounds as a prerequisite for infection in M. fructigena renders the concentration of conidia on the fruit skin less important for brown rot development in pome fruit. By contrast in stone fruits, M. fructicola is able to infect non-injured fruit as they mature and the density of conidia on the fruit’s surface is very important in determining whether or not infection occurs (Corbin, 1963). Thus, the proportion of aerially dispersed conidia causing infection in stone fruit culture might be higher than that in pome fruit culture, indicating a further difference in the epidemiology of Monilinia fruit rot disease in pome in stone fruits. The clear peak in aerial spore content observed in 1997 in the beginning of August may have been due to a distinct change in weather. A period of rainy, dull weather was followed by 4 weeks of hot, dry weather during August 1997. In 1998 there was no clear peak observed over the sampling period. Although a slight peak in spore concentration was observed during the afternoon hours, the absolute differences between afternoon hours and night hours were small. In a similar study with apple powdery mildew, Sutton and Jones (1979) distinguished days in which the highest wind speed and

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lowest relative humidity during mid-day was associated with a typical diurnal pattern of spore catches, from days with similar weather conditions but no discernable periodicity. Similar findings have been reported for powdery mildew of apple (Xu et al., 1995), cherry (Grove, 1998), and grape (Willocquet and Clerjeau, 1998). The diurnal effects are likely to be caused by factors operating on spore dispersal rather than spore production because of the relatively distinct peaks of spore catches in the afternoon. We chose to work with relative humidity (and hence indirectly temperature) and wind speed rather than light intensity because work done to date has identified these factors as the drivers of spore dehiscence and release. We postulated that long-term factors affecting spore production would likely be temperature, and that is why we extended lags of temperature to 180 h. The screening phase of the analysis proved a useful tool for establishing two effects that were having a consistent effect in both years during the study period, but to which we attach differing significance: temperature lagged at 100 h and wind direction with no lag. Increased temperature at 4-day lags increased spore counts in both years, it is possible that, in addition to the diurnal pattern affecting spore release, there is a longer term effect on conidial development. Wind from the west increased spore catches in both years, but this phenomenon is most probably a local one due to the layout of the orchard, rather than having any intrinsic biological significance. The different patterns in effect of wind speed in the 2 years could be due to the different average wind speeds and/or aerial spore concentrations experienced in each year. Holb (2008) also reported an inconsistent relationship of hourly spore counts with wind speed in different orchards. Corbin et al. (1968) found wind speed to be the most important (single) variable, followed by temperature and relative humidity, in explaining the variation in hourly spore catches in an apricot orchard affected by M. laxa. A strong increase in afternoon wind speed was peculiar for the region where the study was conducted (Sacramento Valley, CA, USA) and this might have strengthened the explanatory value of wind speed up to a speed of about 2 m s 1. Similarly, Sutton and Jones (1979) reported a linear increase in spore release with increasing wind speed in the range of 0.7–2.2 m s 1 in Podosphaera leucotricha. In both years of our study, rainfall was restricted to a short period only in the study period so it would be premature to draw too many conclusions on possible effects on M. fructigena. The concentration of spores in the air can be directly influenced by rainfall due to scrubbing although the intensity (<1.5 mm h 1) was much less than the 7–8 mm h 1 causing the sharp decrease in spore concentrations noted by Hirst (1953). It should be noted however that Holb (2008) found that hourly spore counts correlated poorly with rainfall. Relative humidity was only significant in 1998 with an increase reducing spore catches. Spore formation and maturation is influenced by temperature (Hall, 1963), and dehiscence by ambient relative humidity (Byrde and Willetts, 1977). Final take-off of conidia from fruits may then follow instantaneously depending on wind and rainfall conditions. Quantifying the relationship between spores trapped and weather variables using GLM has advantages over other regression approaches: no data transformations were used; there was no bulking of data required to normalize data or to

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remove zero values, and there was no consequent loss in precision; a more realistic underlying error distribution in the spore counts using the Poisson distribution was employed; and predicted spore counts were always in the positive range. We also examined the serial correlation of fitted spore counts with time. Those that occur in temporal proximity tend to be more similar than those that do not because the climatic variables change gradually over time. If all causal variables known to influence spore counts are included, then there should be no correlation between the residuals because only random variation remains. The best model for 1997 did show some systematic correlation between deviance residuals but this problem was not apparent in 1998. The systematic screening method used, in combination with a priori knowledge, identified lagged variables consistently associated with variations in spore counts. The effect of temperature lagged over a period of about 100 h we interpret as an effect on spore production and maturation. The effect of wind direction, although consistent, we interpret to be a purely local effect that may or may not interact with variables such as windspeed, relative humidity and rainfall, reflecting the orchard layout. Although this variable carries no predictive value in general, it is important that these local effects are taken into account when attempting to obtain unbiased estimates of the effects of other, more biologically relevant weather variables. Although wounding from other causes is a pre-requisite for infection by M. fructigena, the relationships obtained will be useful in predictive systems for brown rot development in pome fruit.

Acknowledgements The authors thank research and technical staff of the experimental orchard ‘‘De Schuilenburgh’’ in Kesteren, who offered us the opportunity to do this research; special thanks to Ana Sofia Seixas Basilio for excellent assistance in 1997. The suggestions made by three anonymous reviewers have been invaluable in improving this paper.

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