Temporal development of visual ozone injury on the foliage of Prunus serotina—a statistical evaluation

ENVIRONMENTAL POLLUTION

Environmental Pollution 102 (1998) 287±300

Temporal development of visual ozone injury on the foliage of Prunus serotinaÐa statistical evaluation S. Ghosh a,*, J.M. Skelly b, J.L. Innes a, L. Skelly b a Swiss Federal Institute for Forest, Snow and Landscape Research, ZuÈrcherstrasse 111, CH-8903 Birmensdorf, Switzerland Department of Plant Pathology, Pennsylvania State University, 210 Buckhout Laboratory, University Park, Pennsylvania 16802-4507, USA

b

Received 8 May 1997; accepted 17 March 1998

Abstract Ozone-induced visible injury to plants is relatively common in North America but has rarely been reported in Europe. The south of Switzerland (canton Ticino) has been identi®ed as having high ambient ozone exposures relative to other parts of Switzerland, with accumulated annual exposures over a threshold of 40 ppb (40 nl O3 litreÿ1) for daylight hours from April to September of >30 ppmh. Ozone-induced foliar injury on black cherry (Prunus serotina) has been observed in the area. The main purpose of this study was to establish whether seedlings of black cherry grown in three di€erent treatments (open plots [ambient ozone concentrations], chambers receiving non-®ltered [96% ambient] air and charcoal-®ltered chambers [ca. 60% ambient]) showed signi®cant di€erences with respect to the various aspects of ozone injury development, particularly trends in injury development, the survival probabilities, the average injury level of a tree and the proportion of trees showing any sign of injury over the summer growing season. Visual estimates of the percentage of leaves showing symptoms (adaxial stipple and leaf reddening) on a plant and the percentage of leaf area of only the injured leaves which showed symptoms of ozone injury were recorded weekly for each seedling for eight consecutive weeks. These two scores were multiplied to derive a measure of injury (Y ) which can be interpreted as the proportion of injured leaf area on a tree. This derived score was used in all subsequent analyses. In this paper we assess whether, overall, an increasing trend in the injury levels, as given by the values of Y, was evident. We have also estimated the probability F(t) that the ®rst sign of injury (measured by Y ) might occur after a given number t of weeks. The smaller the value of F(t) for a given t, the stronger the e€ect of the treatment (i.e. the treatment causes relatively more damage). The average injury level of a tree and the proportion of trees that showed any sign of injury over the 8-week period were also analysed. The results from the open plots, the non-®ltered chambers and the charcoal-®ltered chambers were signi®cantly di€erent. However, no statistically signi®cant di€erence was found between the open plots and the non-®ltered chambers. # 1998 Elsevier Science Ltd. All rights reserved. Keywords: Foliage injury; Discoloration; Air pollution; Ozone; Prunus serotina; Survival analysis; Correlation; Nonparametric methods; Analysis of variance

1. Introduction Many fumigation and ®ltration experiments have examined the e€ects of air pollutants on the growth and development of plants, with morphological parameters, such as height, growth and biomass, and physiological phenomena, such as photosynthesis, stomatal conductance and respiration, being popular measures (see Sandermann et al., 1997 for a recent review). In contrast, ®eld studies have mainly examined the occurrence of visual injury on foliage and, in many such studies, there has been a tendency to examine season-long * Corresponding author. Tel: +41-1-739-2431; fax: +41-1-7392215; e-mail: [email protected] 0269-7491/98/$19.00 # 1998 Elsevier Science Ltd. All rights reserved. PII: S0269 -7 491(98)00057 -8

cumulative injury by making a single assessment towards the end of the growing season (e.g. Anderson et al., 1988; Heagle et al., 1994; Hildebrand et al., 1996). With increasing interest in threshold e€ects of cumulative ozone exposure indices, such as AOT40 in Europe (Fuhrer and Achermann, 1994) and SUM60 in the USA (Lee et al., 1988; Lefohn et al., 1988), there is a need to look at the temporal development of foliar injury on species, and when and where such injury occurs. Such data may, however, be dicult to analyse because of the assessment systems that are commonly used and the complications resulting from the premature shedding of symptomatic foliage. This latter phenomenon can actually result in the extent and severity of foliar symptoms on individual trees decreasing over some periods during

288

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

the growing season because of the premature loss of symptomatic foliage. Pollutant-induced visual injury to plants is normally assessed in relation to the foliage surface area that is a€ected. It is often assumed that such injury is cumulative and, on individual leaves, this may be the case. However, following a certain amount of cumulative foliar injury, the physiological functioning of the leaf may be so a€ected that the leaf may be shed (Fredericksen et al., 1995). Premature foliage loss, either with or without visible symptoms of injury, has frequently (but not alwaysÐcf. Wiltshire et al., 1996) been described as a response to injury caused by aboveambient ozone concentrations under experimental conditions, both in broad-leaved trees (e.g. Thompson and Taylor, 1969; Mooi, 1981; Keller, 1988; Mortensen and Skre, 1990; Simini et al., 1992; Wiltshire et al., 1993; Pell et al., 1994) and in conifers (e.g. Coyne and Bingham, 1981; Byres et al., 1992; Stow et al., 1992; Temple et al., 1993). However, outside of the extreme example provided by California (Parmeter et al., 1962; Miller and Millecan, 1971), such foliage shedding has only rarely been reported under ambient conditions (a European example is provided by Keller, 1988). Many studies refer instead to accelerated senescence at the end of the growing season rather than senescence in the middle of the season; e.g. Wiltshire et al. (1993) began their observations of premature leaf fall in August. In contrast, the observations of Keller (1988) began in July, 5 weeks after the start of the experiment. Another feature of many of these cases is that the premature loss of foliage occurred in the absence of visible foliar injury. As a result of the premature loss of symptomatic foliage, a proportion of the a€ected leaf area on each plant may be lost from the ®nal estimate of foliar injury, and the injury can no longer be considered as cumulative. This is particularly true if the plant compensates for the loss of foliage by an acceleration in the production of new foliage (Held et al., 1991). Under certain exposure conditions, it is conceivable that a species with indeterminate shoot growth showing strong ozoneinduced symptom development early in the growing season could have no symptoms by the end of the growing season if ozone concentrations remain low, because all injured leaves have been shed and the youngest foliage has not developed symptoms. As a result, assessments of individual plants' and/or species' sensitivity to ambient ozone exposures using systems that rate foliar injury only once at the end of the season may be inaccurate, with a tendency towards underestimating overall season-long symptom incidence and severity. It is possible to distinguish between various forms of premature loss of foliage. Ozone or other stresses may result in the premature autumnal senescence of foliage, without the development of any visible symptoms of

injury on the foliage (e.g. Wiltshire et al., 1993). Foliage may shed in the middle of the season, also without any indication of visible injury (e.g. Keller, 1988). A third form, and the one that was observed here, is the midseason loss of foliage with visible symptoms of ozoneinduced injury. Such shedding can severely compromise the results of sensitivity tests when only a single assessment of visual injury is made towards the end of the growing season. 1.1. Assessments of foliar injury Visual assessments of ozone-induced injury to foliage usually use a scoring system; e.g. the Horsfall±Barratt rating scheme (Horsfall and Barratt, 1945) was used by Hildebrand et al. (1996). Assessments of visual injury are based on a single characteristic (e.g. stippling on the upper surface of the leaf), or a number of di€erent characteristics may be combined into a single index of injury (e.g. Muir and McCune, 1987). The Horsfall± Barratt rating system has wider classes in the middle of the distribution than at the extremes, the classes being 0, 1±2, 3±5, 6±11, 12±24, 25±49, 50±74, 75±87, 88±94, 95±97, 98±99, and 100% of the foliage. The variation in class width re¯ects the increasing diculty of making accurate and repeatable assessments of the area a€ected by symptoms when the values lie in the middle of the range, a problem that particularly a€ects percentage estimates (e.g. Redman and Brown, 1964; James, 1974; Horsfall and Cowling, 1978; Innes, 1986). The use of ordinal categories of injury complicates subsequent statistical analyses, but the assessments are usually more reproducible than assessments made using linear classes, such as 10% intervals (Muir and McCune, 1987). Published studies indicate some of the problems that can be encountered. Davis and Skelly (1992) examined 2-year-old seedlings of eight eastern hardwood tree species over a single season, making assessments of foliar injury at 4, 8 and 12 weeks after the start of the experimental treatments. The Horsfall±Barratt rating system was also used to assess injury on individual leaves. In cases where defoliation of an individual leaf occurred, the leaf was scored as having 100% injury. No attempt was made to follow the development of symptoms on individual leaves of individual plants. The authors concluded that ®eld surveys should concentrate on the oldest ®ve leaves (on a branchlet) as the leaves of black cherry (Prunus serotina Ehrh.) and yellow-poplar (Liriodendron tulipifera L.) remain sensitive throughout the growing season, resulting in the greatest amount of injury accumulating on the oldest leaves. Simini et al. (1992) examined foliar injury on seedlings of black cherry, yellow-poplar, northern red oak (Quercus rubra L.) and red maple (Acer rubrum L.) on four separate occasions each in 1988 and 1989 (and also in 1990 in the case of black cherry), again using the

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Horsfall±Barratt rating system. The statistical analyses involved data from the last assessment date only, with analysis of variance (ANOVA) being used to determine the presence of di€erences in foliar injury between sites. A similar approach has been adopted in many other studies, with the percentage of leaves injured on each plant (e.g. Karnosky, 1976) or the percentage of leaves multiplied by a severity index (e.g. Karnosky and Steiner, 1981; Bennett et al., 1992) being frequently used. This contrasts with the view of Bennett et al. (1994) who stated clearly that injury severity should be de®ned as the foliar area injured per tree. Bennett et al. (1994) distinguished injury incidence and injury severity, with incidence being the proportion of trees at a site with injury. They analysed the data on injury severity and incidence reported in a number of di€erent studies, using the nonparametric Kruskal±Wallis test for each year within each study. This procedure was adopted because some of the reported measures were on an ordinal scale and many of the distributions were heteroscedastic. Samuelson (1994) examined the occurrence of visible injury on the leaves of seedlings of black cherry and red maple on three separate occasions (14 June, 12 July, 24 August) during the same growing season. On each occasion, all leaves from randomly selected branches extending from the lower third of the main stem, the middle third and the upper third of each stem were assessed on black cherry. Assessments on red maple were made on all leaves along the main stem. Injury was ®rst detected on 12 July, so a comparison of injury levels was only possible for two dates. In the analysis, the foliage was divided into three di€erent ages, based on the position on the tree (lower, middle or upper), with all stages being present on both 12 July and 24 August. As the numbers of leaves were not reported, the e€ects of leaf mortality (if any) on the scores is unknown, and the comparisons did not necessarily refer to the same leaves. Multiple indices have been used on several occasions (see Muir and McCune, 1987 for a review). An example is provided by Temple and Miller (1994), who used a multiple index to evaluate the response of ponderosa pine (Pinus ponderosa Dougl.) to ambient ozone exposures. Injury was scored ``as the average per cent of the needle surface area showing chlorotic mottle or per cent necrosis from 0 to 100% in increments of 10%''. Injury of di€erent needle age classes was evaluated separately. Shed foliage was scored as 100% necrotic. The data were analysed by summing the per cent chlorosis and per cent necrosis on each needle age-class. As the seedlings in the study had three needle age-classes, scores could range from 0 to 300. The index was adjusted to give necrotic foliage a greater weight by multiplying the percentage of necrotic foliage 2±5 times before adding it to the chlorotic percentage. Assessments were repeated

289

on three occasions and, in some cases, the scores for chlorotic mottle injury had decreased between assessments because chlorotic needles had subsequently become necrotic and had abscised. Westman (1989) also published a fairly complex assessment procedure, designed for use in the ®eld with birch (Betula spp.) and other deciduous trees. He recognised eight di€erent colours, six di€erent types of discoloration on individual leaves, the distribution of the discoloration within the crown (eight classes) and the distribution on branches (three classes). The extent of the discoloration was recorded in 10% classes of the existing leaf mass. No indications were provided on how such data should be analysed. In this paper we address several problems in scored foliar injury data that led us to use non-parametric rank-based methods and demonstrate how it is possible to answer some well-posed questions even though the data at hand may be statistically `dicult'. In particular this can consist of several very short series (serially correlated observations for each tree), with many zeros (or non-responses) in the earlier weeks, non-normal probability distribution of the underlying data and diculties arising from the interpretation of the injury scores themselves, possibly resulting from unrecorded abscission of leaves. 2. Experimental material The data used in this study were derived from an open-top chamber experiment conducted in southern Switzerland in 1995. This area has been identi®ed as having high ozone levels, with the numbers of hours with mean ozone concentrations of >60 ppb (60 nl O3 litreÿ1) ranging from 505 to 830 per annum in the period 1989±95 (Das Nationale Beobachtungsnetz fuÈr Luftfremdsto€e, NABEL, 1996; Sta€elbach et al., 1997). These ®gures are substantially higher than any other site with long-term records in Switzerland, although some high-altitude Swiss sites are now known to have even greater number of hours at >60 ppb. May±July AOT40 values for periods with >50 W mÿ2 solar radiation were also higher in southern Switzerland than any other part of the country (1995: ca. 20 ppmh), although the April±September 24-h AOT40 values (1995: ca. 33 ppmh) were exceeded at highaltitude sites. Six plots were used: two ®ltered (activated carbon) and two non-®ltered open-top chambers (Heagle et al., 1973) and two open plots. The ozone concentrations, expressed as weekly summations of the 24-h AOT40 values for the period of assessment, are given in Table 1 Each plot contained 24 black cherry seedlings planted in the ground shortly after germination. All seedlings were derived from seed batches obtained from speci®c

290

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Table 1 Ozone concentrations in the di€erent treatments prior to and during the foliar injury assessment period for black cherry growing within open plots and open-top chambes at a forest nursery site in canton Ticino Week

Plot 1 CF

Plot 2 NF

Plot 3 NF

Plot 4 Ambient

Plot 5 CF

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

0 11 11 11 11 71 204 260 669 1227 1969 2672 2692 2692 2692 2692 2692

134 1270 1545 1815 2395 4064 6983 8581 11 169 14 805 18 114 20 533 21 805 22 269 22 838 23 053 23 211

154 1302 1576 1844 2432 4055 6921 8512 11 070 14 559 17 778 20 134 21 406 21 838 22 382 22 595 22 750

342 2142 2627 3205 4204 6534 10 377 12 584 15 902 20 346 24 347 27 217 28 978 29 563 30 387 30 743 31 035

2 83 93 93 103 331 856 1089 1758 2554 3600 4636 4721 4725 4735 4735 4735

Cumulative weekly values for AOT40 (ppbh) are given. Ozone was measured in only one of the open plots. The injury assessments were undertaken in calendar weeks 27 to 34. CF, charcoal-®ltered chamber; NF, non-®ltered chamber; Ambient, Open-air plot.

mother trees, with foliar injury being assessed on each mother tree at the time of seed collection. All mother trees were located in the canton of Ticino in southern Switzerland. For this analysis, all seedlings within a plot have been analysed together. Assessments of symptom development began on 31 July 1995. Foliar injury on black cherry is typically observed as a dark pigmented adaxial stipple with accompanying leaf reddenning and premature senescence of seriously a€ected leaves (Davis and Skelly, 1992; Skelly et al., 1998). Each plant was assessed for the severity and extent of the symptoms; severity was assessed as the average area of stipple on symptomatic leaves and extent as the percentage of symptomatic leaves on the plant. Visual assessments of each plant were conducted weekly, with the last assessment taking place on 18 September 1995. All assessments were made by the same observer. The chambers, ®ltration systems and ozone monitoring were maintained after this date, but no further assessments of visual injury were conducted in 1995. The scores in the assessments followed the Horsfall±Barratt scoring system used in the ForestHealth Expert Advisory System computer program (Nash et al., 1992a, b). Prior to the start of each scoring session, a calibration exercise was undertaken using the ForestHealth program. Scores were recorded separately for the occurrence of stippling and other foliar injuries for each plant on each visit. While the stippling

appears to be uniquely associated with ozone on black cherry at the site in question, other foliar injuries arise from a variety of pathogens, with hail damage and the fungus Phloeosporella padi (Lib.) v. Arx (syn. Cylindrosporium padi (Lib.) Karst.) being particularly important in the trees being investigated. The resulting scores for foliar injury were used in this paper to examine the nature of such data and to identify appropriate statistical techniques for their analysis. Although three species were present in the treatments (European beechÐFagus sylvatica L.; European ashÐ Fraxinus excelsior L.; European sycamoreÐAcer pseudoplatanus L.), this analysis was restricted to black cherry as this was by far the most sensitive of the species, showed the best development of foliar injury in 1995 and had the greatest number of plants per chamber (the other species were each represented by only four individuals). 3. Methods: statistical formulation of the problem 3.1. DataÐthe injury scores Visual estimates of the percentage of leaves showing symptoms on a plant (X1) and the percentage of leaf area of only the injured leaves which showed symptoms of ozone injury (X2) were recorded weekly for each tree for 8 consecutive weeks. The two scores were multiplied to derive a measure of injury Y, for each tree. The Y scores were used in all subsequent analyses. For every tree, the various values of Y over consecutive weeks constitute a time series. In particular, if Y(t) and Y(s) denote two observations of Y from weeks t and s, then the correlation (Y(t),Y(s)) is not equal to zero. The data used in the analyses were of the form Yij (t) which equals the proportion of leaf area injured on tree i at week t for treatment j, where t=1,2, . . . 8, and j=1,2,3. The Y values were obtained by multiplying X1,ij (t) and X2,ij (t), which were estimates of the proportion of leaves injured, and of the expected injured leaf area on an injured leaf, respectively, both recorded as percentages. Three treatments were of interest: j=1, open plots (ambient ozone); j=2, chambers receiving non-®ltered (96% ambient) air supplies; and j=3, charcoal-®ltered chambers receiving charcoal-®ltered (ca. 60% ambient) air. There were two replicates of each treatment type. Due to power and eciency considerations, the data from the replicates belonging to the same treatment type were pooled together for all analyses. The p-values reported should thus be taken as guidelines only. Some trees died during the experiment, although not apparently because of ozone injury. In all, a total of 46 trees from charcoal-®ltered chambers and 45 trees each from open plots and the non-®ltered chambers were involved in the ®nal analyses (136 trees in total).

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

3.2. Interpretation of the injury scores Y The random variable Y can be interpreted as the proportion of injured leaf area on a tree. This can be seen from the explanation given below. The term A can be used to denote the event that a randomly selected leaf showed injury. It can also be supposed that E(Y/A) and E(Y/Ac) denote the conditional mean of Y given the event A and the conditional mean of Y given the complement Ac (Ac being the event that the randomly selected leaf shows no injury), respectively. Here, E(Y/A) is the expected leaf area on an injured leaf and E(Y/Ac) is the expected leaf area on an uninjured leaf. Clearly E(Y/Ac) is necessarily zero so that: E…Y † ˆ E…Y=A†
P…A† ‡ E…Y=Ac †
P…Ac †; implies E…Y † ˆ E…Y=A†
P…A†:

…1†

This shows that since X1 is an estimate of P(A), the proportion of leaves injured, and X2 an estimate of E(Y/A), the expected injured leaf area on an injured leaf, Y=X1.X2 can be interpreted as an estimate of the expected proportion of leaf area injured on a tree. In what follows, the X1 and the X2 values are in percentages and the Y values are obtained by multiplying X1 and X2. 3.3. Characteristics of the data Table 2 gives some summary statistics that were calculated by pooling all observations from all weeks and Table 2 Summary statistics for the percentage of leaves showing symptoms on a plant (X1), the percentage of leaf area of only the injured leaves which showed symptoms of ozone injury (X2), and the proportion of injured leaf area (Y ) for the three treatments Variable

n

Mean Median

Minimum

Maximum

SD

X1 Ambient NF CF

332 350 368

26.57 18.84 1.09

30 10 0

0 0 0

100 100 40

25.26 21.27 5.24

X2 Ambient NF CF

332 350 368

8.58 6.5 0.21

6 3 0

0 0 0

75 50 6

11.61 10.43 0.96

Y Ambient NF CF

332 350 368

416.9 270.6 4.04

150 30 0

0 0 0

4500 3000 180

645.3 485.9 21.59

The ®gures in this table were computed by pooling the data from all days and both replicates for each treatment. The X1 and X2 values are in percentages and the Y values were obtained by multiplying X1 and X2. Ambient, open-air plot; NF, non-®ltered chamber; CF, charcoal®ltered chamber.

291

Fig. 1 shows the histogram for these data for the injury scores Y for open plots and non-®ltered chambers. Because of a disproportionately larger number of zeros, the distribution for the charcoal-®ltered chambers is better presented in tabular form (Table 3). In particular, it should be noted that: 1. the injury scores are de®ned on ordinal categories; 2. the period of evaluation was small, with only eight data points in each time series corresponding to individual trees; 3. as some time had to pass before the ®rst injury could occur, the distribution of the injury scores for each tree was very skewed, often with many zeros in the ®rst part of the time series; and 4. as the injury levels in each tree were recorded only once per week, it was not possible to record the time when a leaf was shed or when the development of new leaves occurred, both of which might have a€ected the injury scores. Because of (1) and (2), it was dicult to carry out a detailed time series analysis of the data. As a result of (3), it was dicult to make speci®c assumptions about the data distributions, such as the assumption of normality required for an analysis of variance or a t-test. Non-parametric rank-based methods can, however, be used in such situations (Bennett et al., 1994; Krupa et al., 1993) as these methods do not require speci®c assumptions on the shape of the underlying probability distributions, except for the assumption of independence of the observations on the underlying random variable (Fraser, 1976). We describe below how this was carried out. In this context, it should be noted that when an ANOVA is applied directly on serially dependent injury scores (which was not done in this paper), the p-values should be interpreted with caution since the p-values are computed under the assumption of independence (e.g. see the results in Krupa et al., 1993). Because of (4), an analysis of the temporal development of the overall injury status of the tree or an analysis of the average injury level of the whole tree would be a€ected by the ¯uctuations in the data that may be attributable to, for example, the shedding or development of leaves. Alternatively, the ®rst sign of injury (that is the ®rst week when a positive injury score is recorded) can be analysed, which would be less a€ected by the development of new leaves or abscission of leaves at a later stage. These issues are explained below in detail. 3.4. Assessment of trend in the injury levels The Horsfall±Barratt rating system assesses foliage injury in ordinal categories. Spearman's rank correlation (rs) was, therefore, used to measure the monotone

292

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Fig. 1. Histograms of the ozone injury Y in black cherries (Prunus serotina) in the non-®ltered chambers and open plots over the period of 8 weeks. Table 3 Frequency distribution of injury scores (Y ) in the charcoal-®ltered chambers Y 0 15 30 60 90 120 180

Frequency 349 3 5 2 3 3 3

association between the values of Y and time (weeks) for each individual tree and was used as the basic measure of trend in the injury levels. For each tree, the rank correlation measured how the injury level of the whole tree developed over time. Pearson's product moment correlation could have been used to measure the linear association between the injury levels and time but is inappropriate because of the categorical nature of the injury scores. Apart from the reason that the injury scores were de®ned on ordinal categories, the rank correlation rs is more general than Pearson's product moment

correlation, as it measures monotone association (Pirie, 1982). As we were not speci®cally interested in determining whether a linear relationship between time and Y existed, the use of the rank correlation seemed most appropriate. It should be noted that rs and r (Pearson's correlation) measure di€erent and speci®c forms of dependence and, hence, a comparison can be misleading (Pirie, 1982). Further discussions and references to the literature on rs can be found in Pirie (1982). For a recent application of rs in forestry, see Ghosh et al. (1997). With longer time series and provided that the data had come from a Gaussian distribution, standard time series analysis, including the modelling of a speci®c trend function, could have been performed. However, the categorical injury scores create diculties for the application of standard time series methods. The use of rank correlations enables trends in the injury scores to be quanti®ed. Krupa et al. (1993) reported the results of a spectral coherence analysis in a similar context. However, the computation of coherence is based on the assumption of stationarity and is not valid when there may be trend present in the time series (Priestley, 1981, p. 662), as in our case. In our data, many trees showed an overall trend in the injury scores (Fig. 2a). Spectral analyses also require large samples (long time series)

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

293

Fig. 2. Development of the ozone-related injury Y for two black cherries (Prunus serotina); (a) in a non-®ltered chamber showing an upward trend, and (b) in an open plot showing reduction in the overall injury score over time possibly due to abscission of injured leaves or birth of new leaves.

without which high precision cannot be achieved for the estimation of the spectral density, since these methods are based on asymptotic (large sample) theories. Each of the time series in this study had, at most, eight data points. 3.5. First sign of injury We de®ned F(t) as the probability that the ®rst sign of injury would occur after t weeks. This meant that the probability that the ®rst sign of injury would occur within the ®rst t weeks is 1ÿF(t). The sooner the injury occurs, the stronger the e€ect of the treatment on the tree. In other words, for a given value of t, the smaller the value of F(t), the stronger the e€ect of the treatment on the trees. F(t) is known in the literature as the `survival probability distribution function' (Kalb¯eisch and Prentice, 1980; Cox and Oakes, 1984). Within the current context, F(t) estimates the probability that the ®rst sign of injury `does not' occur within the ®rst t weeks, i.e. the tree survives' the ®rst t weeks. In this analysis the only information that was used was the time when the ®rst sign of injury became visible. The analysis is not a€ected if the injured leaf is later abscised or if new leaves develop, reducing the injury level for the whole tree. 3.6. Any sign of injury over 8 weeks Instead of recording the time when the ®rst sign of injury becomes visible it is also possible simply to identify a tree as soon as an injury becomes visible and

compute the proportion of such trees per treatment. The larger the proportion the stronger the e€ect of the treatment. Here also, the analysis remains una€ected if the overall injury level of a tree ¯uctuates over time. 3.7. Average injury We also computed the average injury score for each tree over 8 weeks. This measured the overall mean injury in a tree over the whole 8-week period, as opposed to rs which measured how the injury level of the whole tree developed in time. Again, a larger score implied more damage. In summary, the following issues were of interest: Assessment of trend: 1. quanti®cation of monotone trend in the injury scores for each tree; 2. estimation of the proportion of trees showing an upward monotone trend in overall ozone-related injury, for each treatment type; and 3. comparison of the three treatments with respect to di€erences in trend in ozone-related injury. Assessment of the ®rst sign of injury: 4. estimation of the survival probability distribution function F(t)=Probability (the ®rst sign of injury would occur after t weeks) for each treatment type; and 5. Comparison of the three treatments with respect to di€erences in the survival probability distribution functions.

294

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Assessment of any injury: 6. computation of the proportion of injured trees for each treatment and comparison of the three treatments with respect to di€erences in the proportion of injured trees. Assessment of average injury: 7. computation of summary statistics on the average injury in a tree over time and comparison of the three treatments with respect to di€erences in the mean injury in a tree. 4. Results 4.1. Quanti®cation of monotone trend A measure of the monotone trend in the time series {Y(t), t=1,2,. . .,8} is given by the Spearman's rank correlations rs and de®ned by rs=correlation (t, rank [Y(t)]). This index rs assumes values in the interval (ÿ1, 1), with rs =0 indicating no monotone change in Y in time and rs close to +1 or ÿ1 indicating a strong positive (increase) or negative (decrease) trend in the injury score Y with time in the 8 weeks being considered. The correlation can be calculated for each tree so that tree i in treatment j is given an index rs=rs:ij. Fig. 3 shows the distribution of rs:ij values for all trees and treatments. Several trees have rs:ij values above

zero, indicating an upward monotone trend in overall ozone-related injury. 4.2. Estimation of the proportion of trees showing an upward monotone trend in overall ozone-related injury for each treatment type Corresponding to treatment j, we calculated pj, the proportion of trees for which rs:ij is greater that zero, so that pj is the proportion of trees for treatment j that exhibit an upward trend. Then [pj (1ÿpj)/nj] gives the approximate 95% con®dence interval for pj for treatment j, where nj denotes the total number of trees corresponding to treatment j. The results are presented in Table 4, although these should only be taken as a guideline as within-treatment correlations may be present. 4.3. Comparison of the three treaments with respect to di€erences in trend in ozone-related injury We assume the following model for tree i under treatment j: rs:ij ˆ j ‡ eij ;

…2†

where the eij are independently and identically distributed random variables. The location parameter  j is the e€ect of treatment j. The null hypothesis being tested here is:

Fig. 3. Histogram of the rank correlations for the black cherries (Prunus serotina) in all three treatments combined.

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

295

Table 4 Point estimate ( p), standard deviation of p (SD( p)), and approximate 95% con®dence intervals in each treatment for the proportion of trees showing monotone positive growth in ozone-related injuries over the 8 weeks considered Treatment

No. of trees with r>0

Sample size

p

SD( p)

Upper limit

Lower limit

6 27 26

46 45 45

0.13 0.6 0.58

0.05 0.07 0.07

0.23 0.74 0.72

0.03 0.46 0.43

Charcoal-®ltered Non-®ltered Ambient

Table 5 Summary statistics on the Spearman's rank correlations under the three treatments Charcoal-®ltered chambers

Non-®ltered chambers

Open plots

46 ÿ0.41 0.63 0.0 0.02 0.17

45 ÿ0.58 0.99 0.41 0.37 0.44

45 ÿ0.78 0.98 0.17 0.26 0.39

Number of trees Minimum Maximum Median Mean Standard deviation

Ho : 1 ˆ 2 ˆ 3 ; against the alternative: H1 : at least one of the location is different:

Using the Kruskal±Wallis rank sum test on the estimated rank correlations, the test statistic was 20.4 with two degrees of freedom (corresponding to three treatments). The p-value was 3.7210ÿ5, as compared with a chi-square distribution with two degrees of freedom (Hollander and Wolfe, 1973), indicating that at least one of the location parameters is signi®cantly di€erent from the others. The same procedure was applied on pairs of treatments to identify which of the  j may be di€erent. The p-values were nearly zero for comparison between non®ltered versus charcoal-®ltered chambers (Kruskal± Wallis chi-square=17.92, df=1) and for open plots versus charcoal-®ltered chambers (Kruskal±Wallis chisquare=13.31, df=1), whereas it was 0.18 for open plots versus non-®ltered chambers (Kruskal±Wallis chisquare=1.78, df=1). The results indicate no signi®cant di€erence between open plots and non-®ltered chambers but that the charcoal-®ltered chambers had signi®cantly di€erent trends in ozone-related injury from the open plots and non-®ltered chambers. The use of standard ANOVA for detecting signi®cant di€erences between the three treatments also gave similar results. For the ANOVA, the model and the hypotheses being tested were as in model Eq. (2) above, except that  j was interpreted as the expected value (mean) of rs:ij with the added assumption of normality of the residuals. The observed value of the statistic was

11.76 with two degrees of freedom. The p-value is 1.9910ÿ5, indicating that at least one of the population means was signi®cantly di€erent. Again, to identify which of the means were di€erent, two sample t-tests gave the p-values to be nearly zero for comparison between the charcoal-®ltered chambers and the open plots and between the charcoal-®ltered and non-®ltered chambers. No signi®cant di€erence ( p-value=0.18) was found between the means of the open plots and the non®ltered chambers. Table 5 gives some summary statistics on the Spearman's rank correlations for black cherries under the three treatments. Thus, ÿ0.41 is the minimum rank correlation in the charcoal-®ltered chambers, 0.37 is the sample mean of the rank correlations in the non-®ltered chambers, etc. 4.4. Estimation of the survival probability distribution function F(t) (the probability that the ®rst sign of injury would occur after t weeks) for each treatment type Survival analysis can be used to estimate the survival probability distribution function F(t), that the ®rst sign of injury will occur after t weeks or the probability that the ®rst sign of injury does not occur within the ®rst t weeks, i.e. the tree `survives' the ®rst t weeks without visible injury. The sooner the injury occurs, i.e. the smaller the value of F(t), the stronger the e€ect of the treatment on the trees. For this analysis, the observation on each tree was the week in which the ®rst sign of injury was recorded. Kaplan±Meier estimates (Kalb¯eisch and Prentice, 1980) were used to calculate the survival curves F(t). Table 6 gives the estimates of the probabilities F(t) that the injury will occur after t weeks along with approximate 95% con®dence limits (see Kalb¯eisch and Prentice [1980] for further details). Fig. 4 shows the various plots of the survival probability distribution curves and their con®dence bands for the three treatments. 4.5. Comparison of the three treatments with respect to di€erences in the survival probability distribution functions It was also of interest to test whether the three treatments di€ered with respect to how fast the injury occurred on the trees, i.e. with respect to the survival

296

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Table 6 Kaplan±Meier estimates of survival probability curves for the three basic treatments Treatment

Time (week)

Estimated P(T>t)

Lower 95% con®dence limit

Upper 95% con®dence limit

Charcoal-®ltered chamber

2 5 6 8

0.89 0.87 0.85 0.83

0.81 0.78 0.75 0.72

0.99 0.97 0.96 0.94

Non-®ltered chamber

1 2 3 4 6 7

0.73 0.53 0.44 0.35 0.33 0.3

0.62 0.41 0.32 0.24 0.21 0.19

0.88 0.7 0.62 0.52 0.5 0.47

Open plots

1 2 3 4 5 7

0.53 0.42 0.38 0.36 0.33 0.3

0.41 0.3 0.26 0.24 0.22 0.19

0.7 0.59 0.55 0.54 0.5 0.48

The table shows estimates and 95% con®dence intervals for F(t)=P(T>t) (t in weeks) where T=the week when the ®rst damage occurs. Smaller value of F(t) for a given t is indicative of stronger treatment e€ect (causes more damage).

probabilities. The Harrington and Fleming test (Harrington and Fleming, 1982; Anderson et al., 1993) was applied to the pairs of treatment types. The Harrington and Fleming test is a non-parametric test (a chi-square test) based on the Kaplan±Meier estimates of F(t). Table 7 shows the p-values and related information; as before, a small p-value indicates a signi®cant di€erence between the treatment pairs with respect to their survival probability curves. 4.6. Computation of the proportion of injured trees for each treatment and comparison of the three treatments with respect to di€erences in the proportion of injured trees Injured trees were identi®ed as trees that displayed any sign of injury (i.e. a positive value of Y was recorded at least once over the 8-week period). For each treatment, the proportions were computed as 0.17 for the charcoal-®ltered chambers (46 trees) and 0.69 for both the non-®ltered chambers and open plots (45 trees for each of these treatments). As expected, the charcoal®ltered chambers had signi®cantly smaller numbers of injured trees than the other two treatments. 4.7. Computation of summary statistics on the average injury to a tree over time and comparison of the three treatments with respect to di€erences in the mean injury to a tree The average injury score was computed for each tree under each treatment (Table 8). De®ning mij to be the average injury level for tree i in treatment j in order

to compare the three treatments, the appropriate model is: mij ˆ j ‡ eij :

…3†

The null hypothesis to be tested here is: Ho : 1 ˆ 2 ˆ 3 ; against the alternative: H1 : at least one of the location parameters is different: The Kruskal±Wallis rank sum test indicated a test statistic (chi-square) of 38.93 with two degrees of freedom with a p-value of zero. Thus, the three treatments are signi®cantly di€erent. A similar result was found by the usual ANOVA method (F-statistic=12.68 and a near zero p-value). Pairs of treatments were also compared; no signi®cant di€erence was found between the open plots and the non-®ltered chambers by Kruskal± Wallis rank sum test ( p-value=0.37) or by two-sample t-tests ( p-value=0.17). The charcoal-®ltered treatment was signi®cantly di€erent from the other two treatments, using both testing methods. 5. Discussion 5.1. Trend in ozone injury Table 4 provides estimates and approximate 95% con®dence intervals for the proportion of trees showing

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

297

Fig. 4. Kaplan±Meier estimates of survival curves showing the development of the discoloration of black cherry (Prunus serotina) under di€erent treatments. Table 7 Pairwise comparison of survival probability curves for the three basic treatments using Kaplan±Meier estimates Treatment

n

Charcoal-®ltered vs non-®ltered

46

Charcoal-®ltered vs open plot

46

Non-®ltered vs open plot

45

45

45

45

Chi-square statistic (1 df )

p-value

28.5

9.56eÿ08

29.8

4.79eÿ08

0.2

0.6188

n, number of trees.

an upward (monotone) trend in visible ozone-related injury for the various treatment types. The observations from similar treatment types were pooled for the analysis. As mentioned earlier, the estimates of the standard deviations and hence the con®dence intervals should be taken as guidelines only, due to the simplicity adopted in calculating the standard deviation. Nevertheless, in the open plots and the non-®ltered chambers the

proportions of trees showing an upward trend in such symptoms were quite large, about 57.8 and 60% respectively. The con®dence intervals were also clearly on the positive side of the real line; they did not include zero so the proportion for the open plots and the non®ltered chambers were signi®cantly larger than zero. The results were less clear for the charcoal-®ltered chambers since the lower limit for this treatment type was very close to zero; however, the con®dence interval was mostly on the positive side of the real line, indicating possible evidence of a cumulative but smaller e€ect of ozone in comparison to the other two treatments. There were signi®cant di€erences between treatments, using both the non-parametric Kruskal±Wallis method and standard ANOVA. In particular, the charcoal®ltered chambers were signi®cantly di€erent from the other two; no signi®cant di€erence was found between the non-®ltered chambers and the open plots. 5.2. Survival probability distribution function F(t) Table 6 gives the estimates of F(t) (t in weeks). As indicated above, smaller F(t) values indicate that there is more injury from a particular treatment. The survival curve for plants in the charcoal-®ltered chambers

298

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Table 8 Summary statistics on the mean injury level (Y ) on the trees for the three treatments Treatment

n

Mean

Median

Minimum

Maximum

SD

Charcoal-®ltered Open plot Non-®ltered

46 45 45

4.04 401.3 263.1

0 123.8 56.25

0 0 0

90 2280 1418.8

15.02 542.5 384.2

n, number of trees.

decreased at a slower rate than in either the non-®ltered chambers or the open plots (Fig. 4). Thus, for example, the probability of observing the ®rst damage after 2 weeks was about 0.89 for the charcoal-®ltered chambers, 0.53 for the non-®ltered chambers and 0.42 for the open plots. The probability that the ®rst damage occurred after 7 weeks is more than 0.82 for the charcoal-®ltered chambers, and 0.30 for both the non-®ltered chambers and the open plots. The di€erence between the charcoal-®ltered treatments and the other two treatment types was larger than the di€erence between the open plots and the non-®ltered chambers, as would be expected given the relative ozone exposures. With increased monitoring time, the upper tail of the F(t) curves (i.e. the probability that the damage will be observed after a greater number of weeks) would be estimated better. The F(t) curves for the various cases were compared using hypothesis testing. The p-values (Table 7) con®rmed that the amount of foliar injury observed on seedlings within the charcoal-®ltered chambers was different from the other two treatments, whereas there was no indication that the amount of injury within the open plots and non-®ltered chambers di€ered signi®cantly. 5.3. Proportion of damaged trees Over the 8-week period, 17.4% of the trees in the charcoal-®ltered chambers showed some sign of injury. In both the open plots and the non-®ltered chambers 69% of the trees showed injury. Thus, while the charcoal-®ltered chambers had a signi®cantly lower number of damaged trees, a longer observation period would be required to identify any di€erence between the open plots and the non-®ltered chambers. 5.4. Mean injury level per tree Table 8 provides the statistics on average injury level per tree. Here also, the open plots show, on average, a higher injury level (401.3) than the non-®ltered (263.1) and charcoal-®ltered chambers (4.04). The open plots also had a larger standard deviation than the non®ltered chambers. As a result, a signi®cant di€erence could not be identi®ed between the e€ects of the open plots and the non-®ltered chambers, either by the Kruskal±Wallis or standard ANOVA methods. These

results refer to the mean levels of injury in the plots. Substantial di€erences were encountered in the sensitivities of the di€erent families, but the experiment was not designed to investigate this. Only two seedlings from each family were present in each plot, and the sample sizes were, therefore, insucient to conduct any useful statistical analysis of the within-plot variability. 6. Conclusions The results presented here clearly indicate the in¯uence of ozone on the development of foliar injury on black cherry in southern Switzerland. As the concentrations are particularly high, charcoal-®ltering still results in seedlings in ®ltered chambers receiving a suf®cient dose to induce foliar injury. However, the extent of injury is very much reduced in comparison to either open plots or non-®ltered chambers, and its onset occurs later in the season. The ozone concentrations in the non-®ltered chambers were slightly less (ca. 96%) than those in the open plots, and resulted in lower injury levels. However, the extent to which this was attributable to the reduced ozone concentrations as opposed to lower light levels, for example, is not known. The analysis of weekly data on foliar injury is complex. Unlike the growth of woody biomass, injury is not necessarily cumulative over time. Instead, the extent of injury on the leaves can vary between assessments, depending on whether any injured leaves have been shed. In the majority of cases, such premature leaf shedding cannot be determined without repeated assessments. A further complication for the statistical analysis is that the assessments are often made using ordinal scales and that, in some cases, the classes used di€er in their widths (such as with the Horsfall±Barratt system). The indices X1 and X2 are not de®ned unambiguously; e.g. the values ¯uctuate with time instead of growing monotonously, as would be expected with the cumulative development of injury. This could be due to a number of reasons, including the loss of injured leaves or reduction of an injury score because of the development of new leaves. The problem could be partially avoided by, for example, adding information on rates of loss of leaves and the recruitment of new leaves to the population. A simpler remedy, which would have been used here had this speci®cally been a study of premature

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

leaf loss, would be to mark individual leaves and observe them until they were shed. This way, new leaves could also be added to the investigation. A number of di€erent assessment methods are available. Generally, the use of an aggregated index involves the loss of information (Muir and McCune, 1987) and complicates any statistical analyses that may be undertaken with the data, although such indices may help the visualisation of the relative injury between trees. The most appropriate methods of analysis depend on the measurements that have been undertaken and the questions being asked. In species where foliar injury may be followed by leaf abscission, the use of survival probability distribution functions is particularly recommended. In future investigations of foliar injury development it would be particularly useful to undertake repeat assessments at frequent intervals. It would be also of considerable interest to assess and analyse the injury on particular leaves of plants over the season. Such a procedure would be extremely labour-intensive, but would provide major bene®ts in that the rate of development of the injury could be assessed. If the assessments of the individual leaves were made quantitatively, such as by image analysis of photographs of each leaf, then the value of the study would be even greater. Developments in scanning techniques and image analysis software mean that such procedures are increasingly possible, and large numbers of leaves could be quantitatively assessed fairly quickly. Acknowledgements Technical support during this experiment was provided by Christian Hug and Peter Bleuler. Werner Landolt provided the ozone data and the fungal identi®cation was made by Roland Engesser. Their contributions are gratefully acknowledged.

References Anderson, R.L., Brown, H.D., Chevone, B.I., McCartney, T.C., 1988. Occurrence of air pollution symptoms (needle tip necrosis and chlorotic mottling) on eastern white pine in the Southern Appalachian Mountains. Plant Disease 72, 130±132. Anderson, P.K., Borgan, O., Gill, R.D., Keiding, N., 1993. Statistical Models Based on Counting Processes. Springer, New York. Bennett, J.P., Rassat, P., Berrang, P., Karnosky, D.F., 1992. Relationships between leaf anatomy and ozone sensitivity of Fraxinus pennsylvanica Marsh. and Prunus serotina Ehrh. Environmental and Experimental Botany 32, 33±41. Bennett, J.P., Anderson, R.L., Mielke, M.L., Ebersole, J.J., 1994. Foliar injury air pollution surveys of eastern white pine (Pinus strobus L.): a review. Environmental Monitoring and Assessment 30, 247±274.

299

Byres, D.P., Dean, T.J., Johnson, J.D., 1992. Long-term e€ects of ozone and simulated acid rain on the foliage dynamics of slash pine (Pinus elliottii var. elliottii Engelm.). New Phytologist 120, 61±67. Cox, D.R., Oakes, D., 1984. Analysis of Survival Data. Chapman and Hall, London. Coyne, P.I., Bingham, G.E., 1981. Comparative ozone dose response of gas exchange in a ponderosa pine stand exposed to long-term fumigations. Journal of the Air Pollution Control Association 31, 38±41. Davis, D.D., Skelly, J.M., 1992. Foliar sensitivity of eight eastern hardwood tree species to ozone. Water, Air, and Soil Pollution 62, 269±277. Fraser, D., 1976. Probability and Statistics, Theory and Applications. DAI Press, Toronto. Fredericksen, T.S., Joyce, B.J., Skelly, J.M., Steiner, K.C., Kolb, T.E., Kouterick, K.B., Savage, J.E., Snyder, K.R., 1995. Physiology, morphology, and ozone uptake of leaves of black cherry seedlings, saplings, and canopy trees. Environmental Pollution 89, 273±283. Fuhrer, J., Achermann, B., 1994. Critical Levels for Ozone: a UNECE workshop report. Schriftenreihe der FAC Liebefeld 16. Swiss Federal Research Station for Agricultural Chemistry and Environmental Hygiene, Liebefeld-Bern. Ghosh S., Landmann G., Pierrat, J.C., MuÈller-Edzards, C., 1997. Spatio-temporal variation in defoliation. In: MuÈller-Edzards, C., De Vries, W., Erisman, J.W. (Eds.), 10 Years Forest Condition Monitoring in Europe. United Nations Economic Commission for Europe/European Commission, Geneva, pp. 35±50. Harrington, D.P., Fleming, T.R., 1982. A class of rank test procedures for censored survival data. Biometrika 69, 553±566. Heagle, A.S., Body, E.E., Heck, W.W., 1973. An open-top ®eld chamber to assess the impact of air pollution on plants. Journal of Environmental Quality 2, 365±368. Heagle, A.S., Miller, J.E., Sherrill, D.E., 1994. A white clover system to estimate e€ects of tropospheric ozone on plants. Journal of Environmental Quality 23, 613±621. Held, A.A., Mooney, H.A., Gorham, J.N., 1991. Acclimation to ozone stress in radish: leaf demography and photosynthesis. New Phytologist 118, 417±423. Hildebrand, E., Skelly, J.M., Fredericksen, T.S., 1996. Foliar response of ozone-sensitive hardwood tree species from 1991 to 1993 in the Shenandoah National Park, Virginia. Canadian Journal of Forest Research 26, 658±669. Hollander, M., Wolfe, D.A., 1973. Nonparametric Statistical Methods. John Wiley, New York. Horsfall, J.G., Barratt, R.W., 1945. An improved grading system for measuring plant disease. Phytopathology 35, 655 . Horsfall, J.G., Cowling, E.B., 1978. Pathometry: the measurement of plant disease. In: Horsfall, J.G., Cowling, E.B. (Eds.), Plant Disease. An Advanced Treatise, Vol. 2. How Disease Develops in Populations. Academic Press, New York, pp. 119±136. Innes, J.L., 1986. The use of percentage cover measurements in lichenometric dating. Arctic and Alpine Research 18, 209±216. James, W.C., 1974. Assessment of plant diseases, losses. Annual Reviews of Phytopathology 12, 27±48. Kalb¯eisch, J.D., Prentice, R.L., 1980. The Statistical Analysis of Failure Time Data. John Wiley, New York. Karnosky, D.F., 1976. Threshold levels for foliar injury to Populus tremuloides by sulfur dioxide and ozone. Canadian Journal of Forest Research 6, 166±169. Karnosky, D.F., Steiner, K.C., 1981. Provenance, family variation in response of Fraxinus americana and F. pennsylvanica to ozone and sulfur dioxide. Phytopathology 71, 804±807. Keller, Th., 1988. Growth and premature leaf fall in American aspen as bioindications for ozone. Environmental Pollution 52, 183±192. Krupa, S.V., Manning, W.J., Nosal, M., 1993. Use of tobacco cultivars as biological indicators of ambient ozone pollution: an analysis of exposure±response relationships. Environmental Pollution 81, 137±146.

300

S. Ghosh et al./Environmental Pollution 102 (1998) 287±300

Lee, E.H., Tingey, D.T., Hogsett, W.E., 1988. Evaluation of ozone exposure indices in exposure-response modeling. Environmental Pollution 53, 43±62. Lefohn, A.S., Laurence, J.A., Kohut, R.J., 1988. A comparison of indices that describe the relationship between exposure to ozone and reduction in the yield of agricultural crops. Atmospheric Environment 22, 1229±1240. Miller, P.R., Millecan, A.A., 1971. Extent of air pollution damage to some pines and other conifers in California. Plant Disease Reports 55, 555±559. Mooi, J., 1981. In¯uence of ozone and sulphur dioxide on defoliation and growth of poplars. Mitteilungen der Forstliche Bundesversuchsanstalt Wien 137, 47±51. Mortensen, L.M., Skre, O., 1990. E€ects of low ozone concentrations on growth of Betula pubescens Ehrh., Betula verrucosa Ehrh. and Alnus incana (L.) Moench. New Phytologist 115, 165±170. Muir, P.S., McCune, B., 1987. Index construction for foliar symptoms of air pollution injury. Plant Disease 71, 558±565. NABEL, 1996. La pollution de l'air 1995. Cahier de l'environnement No. 267. Oce feÂdeÂral de l'environment, des foreÃts et du paysage, Berne. Nash, B.L., Saunders, M.C., Miller, B.J., Bloom, C.A., Davis, D.D., Skelly, J.M. 1992a. ForestHealth, an expert advisory system for assessing foliar and crown health of selected northern hardwoods. Canadian Journal of Forest Research 22, 1770±1775. Nash, B.L., Saunders, M.C., Miller, B.J., Bloom, C.A., Davis, D.D., Skelly, J.M., 1992b. The ForestHealth expert advisory system as a diagnostic and training tool for assessing tree health. In: Watson, D.G., Zazueta, F.S., Bottcher, A.B.,(Eds.), Computers in Agricultural Extension Programs. American Society of Agricultural Engineers, St. Joseph, pp. 417±421. Parmeter, J.R., Bega, R.V., Ne€, T., 1962. A chlorotic decline of ponderosa pine in southern California. Plant Disease Reporter 46, 269±273. Pell, E.J., Temple, P.J., Friend, A.L., Mooney, H.A., Winner, W.E., 1994. Compensation as a plant response to ozone and associated stresses: an analysis of ROPIS experiments. Journal of Environmental Quality 23, 429±436. Pirie, W., 1982. Spearman rank correlation coecient. In: Kotz, S., Johnson, N.L. (Eds.), The Encyclopedia of Statistical Sciences. John Wiley, New York, pp. 584±587. Priestley, M.B., 1981. Spectral Analysis and Time Series. Academic Press, New York. Redman, C.E., Brown, I.F., 1964. A statistical evaluation of the Barratt and Horsfall rating system. Phytopathology 54, 904.

Samuelson, L.J., 1994. The role of microclimate in determining the sensitivity of Quercus rubra L. to ozone. New Phytologist 128, 235± 241. Sandermann, H., Wellburn, A.R., Heath, R.L., 1997. Forest Decline and Ozone. A Comparison of Controlled Chamber and Field Experiments. Springer, Berlin. Simini, M., Skelly, J.M., Davis, D.D., Savage, J.E., Comrie, A.C., 1992. Sensitivity of four hardwood species to ambient ozone in north central Pennsylvania. Canadian Journal of Forest Research 22, 1789±1799. Skelly, J.M., Innes, J.L., Snyder, K.R., Savage, J.E., Hug, C., Landolt, W., Bleuler, P., 1998. Investigations of ozone-induced injury in forests of southern Switzerland: ®eld surveys and open-top chamber experiments. Chemosphere 36(4), 995±1000. Sta€elbach, T., Neftel, A., Blatter, A., Gut, A., Fahrni, M., StaÈhelin, J., PreÂvoÃt, A., Hering, A., Lehning, M., Neininger, B., BaÈumle, M., Kok, G.L., Dommen, J., Hutterli, M., Anklin, M., 1997. Photochemical oxidant formation over southern Switzerland 1. Results from summer 1994. Journal of Geophysical Research 102, 23345±23 362. Stow, T.K., Allen, H.L., Kress, L.W., 1992. Ozone impacts on seasonal foliage dynamics of young loblolly pine. Forest Science 38, 102±119. Temple, P.J., Miller, P.R., 1994. Foliar ozone injury and radial growth of ponderosa pine. Canadian Journal of Forest Research 24, 1877±1882. Temple, P.J., Riechers, G.H., Miller, P.R., 1993. Growth responses of ponderosa pine to long-term exposure to ozone, wet and dry acidic deposition, and drought. Canadian Journal of Forest Research 23, 59±66. Thompson, C.R., Taylor, O.C., 1969. E€ects of air pollutants on leaf drop, fruit drop and yield of citrus tress. Environmental Science and Technology 3, 934±940. Westman, L., 1989. A new method for assessment of visible damage to birch and other deciduous trees. In: Bucher, J.B., Bucher-Wallin, I. (Eds.), Air Pollution and Forest Decline, Vol. 1. EidgenoÈssische Forschungsanstalt fuÈr forstliche Versuchswesen, Birmensdorf, pp. 223±228. Wiltshire, J.J.J., Wright, C.J., Unsworth, M.H., Craigon, J., 1993. The e€ects of ozone episodes on autumn leaf fall in apple. New Phytologist 124, 433±437. Wiltshire, J.J.J., Wright, C.J., Colls, J.J., Craigon, J., Unsworth, M.H., 1996. Some foliar characteristics of ash trees (Fraxinus excelsior) exposed to ozone episodes. New Phytologist 134, 623±630.