Conclusive remarks. Reliability and comparability of chlorophyll fluorescence data from several field teams

Environmental and Experimental Botany 73 (2011) 116–119 Contents lists available at ScienceDirect Environmental and Experimental Botany journal homepage: www.elsevier.com/locate/envexpbot Conclusive remarks. Reliability and comparability of chlorophyll fluorescence data from several field teams Filippo Bussotti a,∗ , Martina Pollastrini a , Chiara Cascio a , Rosanna Desotgiu a , Giacomo Gerosa b , Riccardo Marzuoli b , Cristina Nali c , Giacomo Lorenzini c , Elisa Pellegrini c , Maria Giovanna Carucci c , Elisabetta Salvatori d , Lina Fusaro d , Massimo Piccotto e , Paola Malaspina f , Alice Manfredi f , Enrica Roccotello f , Stefania Toscano g , Elena Gottardini h , Antonella Cristofori h , Alessio Fini i , Daniel Weber j , Valentina Baldassarre k , Lorenzo Barbanti l , Andrea Monti l , Reto J. Strasser m a University of Firenze, Dept. of Agricultural Biotechnology, Piazzale delle Cascine 28, 50144 Firenze, Italy Catholic University of Brescia, Dept. of Mathematics and Physics, Brescia, Italy University of Pisa, Dept. of Tree Science, Entomology and Plant Pathology “Giovanni Scaramuzzi”, Pisa, Italy d Sapienza University of Rome, Department of Plant Biology, Roma, Italy e University of Trieste, Dept. of Life Science, Trieste, Italy f University of Genova, DIP.TE.RIS., Polo Botanico Hanbury, Italy g University of Catania, Faculty of Agriculture, Italy h IASMA Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Trento, Italy i University of Firenze, Dept. of Horticulture, Italy j Biodiversity & Climate Research Centre, Frankfurt am Main, Germany k University of Milano, Dept. of Plant Production, Italy l University of Bologna, Dept. of Agroenvironmental Science and Technology, Italy m University of Geneva, Dept. of Plant Biology, Switzerland b c a r t i c l e i n f o Keywords: Chlorophyll fluorescence Field exercises FV /FM Harmonization Intercalibration a b s t r a c t Two field exercises were carried out to compare chlorophyll a fluorescence measurements taken in the field by field teams working on the same project. In the first exercise (2007, Passo Pura, Ampezzo, Udine, Northern Italy) the operators took measurements on the same leaf areas (maintaining fixed leaf clips); in the second (2009, Monterotondo Marittimo, Grosseto, Central Italy) the teams worked independently, but addressing a common research question. The results of the first exercise showed that: (a) FV /FM was stable and had little variation among teams and instruments; (b) the results from the different teams correlated well; (c) the most suitable parameters of fast kinetics analysis are those measured on the normalized fluorescence transients. In the second exercise, when the teams worked independently, the results were much more variable and the correlations between measurements of different operators were weak. These results suggest that field chlorophyll a fluorescence measurements taken by different teams/operators can be comparable only if particular care is taken to the internal variability of the samples and a standardized sampling strategy is applied. A statistically sound representation of a population can be then reached. © 2010 Elsevier B.V. All rights reserved. 1. Introduction In the literature, chlorophyll fluorescence data can be compared only in relative terms (i.e., significance of differences; relative differences between treatment and control), and the comparison of their absolute values might be problematic because of the large variability in instrumental and working conditions. Fluorescence data are usually expressed as ratios (for example the maximum ∗ Corresponding author. E-mail address: filippo.bussotti@unifi.it (F. Bussotti). 0098-8472/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.envexpbot.2010.10.023 quantum yield of primary photochemistry in the dark adapted state, FV /FM , Paillotin, 1976) and/or in arbitrary units, and it is not possible to refer measurements to a specific units. On the contrary, other eco-physiological measurements, such as gas exchange measurements, can be quantitatively compared in an absolute scale. For this reason, datasets from different experiments cannot be merged, and when more instruments and operators work within the same project, great care must be taken in comparing the raw data. In 2007 we started a project aimed at determining the variations of measurements taken by different instruments under the same working conditions and, hence, the limits and possibilities of comparison. Assuming that the fluorimeters employed were in good 117 F. Bussotti et al. / Environmental and Experimental Botany 73 (2011) 116–119 A 1.2 P 4000 Relave variable fluorescence Fluorescence intensity (arbitrary units) 5000 I J 3000 A B C D E 2000 O 1000 B 1.0 0.8 0.4 0.2 0.0 0.01 0 0.01 0.1 1 10 100 A B C D E 0.6 1000 0.1 1 Time [ms] 1000 D C 0.8 0.40 0.7 0.35 Linkage Distance FV /FM (B-F) 100 Eucledian Distance 0.45 0.9 0.6 0.5 0.4 A-B: A-C: A-D: A-E: A-F: 0.3 0.2 0.2 10 Time [ms] 0.3 0.4 0.5 0.6 r = 0.92, p = 0.00 r = 0.90, p = 0.00 r = 0.81, p = 0.00 r = 0.93, p = 0.00 r = 0.91, p = 0.00 0.7 0.8 0.9 B C D E F 0.30 0.25 0.20 0.15 0.10 D C E F B A FV/FM (A) 45 45 35 35 30 30 25 20 15 25 20 15 10 10 A-B: A-C: A-D: A-E: 5 0 -5 -5 F 40 PIABS (B-E) PIABS (B-E) 40 E 0 5 10 15 20 25 30 A-B: A-C: A-D: A-E: 5 r = 0.90, p = 0.00 r = 0.94, p = 0.00 r = 0.73, p = 0.00 r = 0.89, p = 0.00 35 PIABS (A) B C D E 0 -5 -5 0 5 10 15 PIABS (A) 20 r = 0.91, p = 0.00 r = 0.94, p = 0.00 r = 0.73, p = 0.00 r = 0.89, p = 0.00 25 30 35 B C D E Fig. 1. Results of the exercise at Passo Pura (2007). (A) Example of original fluorescence transients measured at the same leaf sample (same leaf clips) by different surveyors. (B) The transients reported in (A) were normalized for F0 and FM . (C) FV /FM measured by the six surveyors. Correlation between the reference team (A) and the others (B–F). Teams A–E used HandyPEA; team F used MiniPAM. (D) Cluster analysis of the different teams, compared according to the variable FV /FM . (E) PIABS measured by the different surveyors. Correlation between the reference team (A) and the others (C–F). The equations of the straight lines are: A–B = 1.73 + 1.12x; A–C = 1.33 + 0.79x; A–D = 1.93 + 0.48x; A–E = 2.23 + 0.83x. (F) The same correlation of (E), after transformation with the formula [(1/mx) − a]. The equations were now: A–B: −0.48 + 1.01x; A–C: 0.35 + 0.99x; A–D: 2.02 + 0.99x; A–E: 0.44 + 0.99x. conditions, differences in the results can derive from their intrinsic characteristics (different adjustments among similar devices), from setting (intensity of the lamp, gain, timing of the data acquisition) and from the dark adaptation (Bussotti, 2007). The activities aimed at ensuring the same results from different operators are called Intercalibration. This activity is needed when different operators and/or research teams are working on the same project (Ferretti et al., 2009; Bussotti et al., 2009). A further aim was to verify the possible influence of the sampling strategy (choice and number of samples) on the results. The question is to verify if, given the same case-study, different surveyors, with different instruments, reach the same conclusions (Harmonization). The exercises were carried out in two different pristine areas during summer 2007 and 2009. We assume that it is possible to implement a real Quality Assurance (QA, Ferretti, 2009) programme 118 F. Bussotti et al. / Environmental and Experimental Botany 73 (2011) 116–119 F0 FM M0 VJ VI FV /FM PIABS Mean St. dev. CV 761 2477 0.92 0.57 0.77 0.67 11.37 179 492 0.11 0.03 0.02 0.03 3.08 23.60 20.32 13.37 5.25 3.49 4.69 29.08 by monitoring the systematic errors deriving from the instruments and sampling strategies employed. 2. The exercise at Passo Pura (2007) The first exercise was held at Passo Pura (Udine, Northern Italy, 46◦ 25′ 21′′ N–12◦ 44′ 38′′ E, 1400 m asl) in summer 2007. Forests of beech (Fagus sylvatica L.) and Norway spruce (Picea abies Karst.) represent the most important plant communities. Six working groups, from the scientific institutions to which the Authors of this paper belong, took chlorophyll a fluorescence measurements with 5 HandyPEA (Hansatech Ins., Norfolk, England) and 1 MiniPAM (Walz, Effeltrich, Germany) fluorimeters. The groups were anonymous and were listed alphabetically (A–F). Forty leaves (belonging to 10 vascular plants chosen at the forest edge, i.e., 4 leaves per plant) were dark adapted, using leaf clips provided for instruments, 20 min before each measurement. All the HandyPEA were fixed to the same settings (maximum lamp intensity: 3000 ␮mol m−2 s−1 ; duration: 1′ ; gain: 1). The results were then elaborated as mean values for each plant, are reported in Fig. 1 and Table 1. Team A, working with a recently calibrated instrument, was considered as reference. An example of fluorescence transients, measured by different HandyPEA on the same leaf sample, is reported in Fig. 1A. The differences between the teams are obvious but, when the transients themselves were normalized between F0 and FM (Fig. 1B), all curves overlapped and, with the exception of team “D”, the differences disappeared. The parameters of the original transients, such as F0 and FM , show a large coefficient of variation (%CV = [St. dev./mean] × 100) (Table 1), whereas the normalized signals VJ and VI (for explanation and formulae see the list in Table 1, Bussotti et al., in this issue), and FV /FM (in this latter case the MiniPAM, team “F”, was included in the elaboration) were comparable, with a fairly low %CV. The relatively high %CV values of M0 reflected the variability of the measurements in the initial slopes. Lastly, the Performance Index, PIABS was the most variable parameter being the combination of three different parameters. Once the variability of the measurements among the instruments (Intercalibrations) was considered, the further question was to verify if the results of the surveyors correlated, although intrinsically subject to a certain degree of variability. Fig. 1C, referring to the most commonly used parameter FV /FM , shows the correlation of each individual surveyor with reference team “A”. The r-values were in most cases higher than 0.9, but this coefficient was a bit lower for the correlation A–D (Fig. 1C). The Pearson coefficients of correlation (r) were high also for PIABS (Fig. 1E), but the linear regressions have very different patter (Fig. 1E). The linear regressions are expressed by y = a + mx equations where “a” is the intercept point on the y-axis, “x” is the measured PIABS value, and “m” a coefficient which expresses the inclination of the line (1 = 45◦ , there are no differences between the two series of measures). Each individual measurement point was aligned by transformation: [(1/mx) − a]. The result is shown in Fig. 1F. By correcting the PIABS in this way, the CV for this parameter decreased from 29% to 9%. The anomaly of “D” in relation to the other teams is confirmed in Fig. 1D. Cluster analysis, carried out considering the parameter FV /FM , shows the distance of “D” team’s results from the others. 3. The exercise at Monterotondo Marittimo (2009) The second exercise was organized in the nearby of Monterotondo Marittimo (Grosseto, Central Italy, 43◦ 09′ 13′′ N–10◦ 51′ 17′′ E, 600 m asl). The main vegetation types are represented by mesophylous broadleaves forests (Castanea sativa Mill., Quercus cerris L.), with the presence of Mediterranean evergreen elements (Quercus suber L., Quercus ilex L.). That exercise was performed at the end of the workshop “Chlorophyll fluorescence: from theory to (good) practice” (May 2009) and was aimed at evaluating the influence of different sampling strategies. The exercise was carried out at the edge of an active geothermal area (Parco delle Biancane). Two sites were chosen (A: directly exposed to the geothermal vapours, and B: control). Three trees were selected in each site: one individual of Q. suber, one Q. cerris and one of their hybrid Quercus crenata. The measurements A 0.9 A 0.8 B 0.7 C 0.6 E 0.5 F G 0.4 H 0.3 I QCE QCR QSU QCE QCR QSU D A B 0.4 Linkage Distance Table 1 Results of the exercise at Passo Pura (2007). Mean, standard variation and coefficient of variation (CV, expressed as % of the mean), among the field teams measuring the same leaf samples, for some of measured parameters. FV /FM included a MiniPAM. Explication of the parameters and relative formulae can be found in Strasser et al. (2004) and Bussotti et al. (in this issue). F0 = Minimal fluorescence from a dark adapted leaf; FM = Maximal fluorescence from a dark adapted leaf; M0 = Slope of the curve at the origin of the fluorescence rise. It is a measure of the rate of the primary photochemistry; VJ = Relative variable fluorescence at 2 ms; VI = Relative variable fluorescence at 30 ms; FV /FM = [FM − F0 ]/FM = Maximum quantum yield of primary photochemistry; PIABS = Performance Index on absorption basis. 0.3 B Eucleidean distance 0.2 0.1 0.0 I F E C G D B H A Fig. 2. Results of the exercise at Monterotondo Marittimo (2009). Behaviour of FV /FM, measured by the nine field teams (A–I) in the two sites (A = geothermal area; B = control) and tree species (QCE = Quercus cerris; QCR = Quercus crenata; QSU = Quercus suber). (B) Cluster analysis of the teams, compared according to the variable FV /FM . F. Bussotti et al. / Environmental and Experimental Botany 73 (2011) 116–119 were made on the current year leaves. The criteria to choose the sample leaves (exposure, light or shade leaves, etc.) were defined autonomously by each group. Nine different groups (anonymous, alphabetically listed as A through I) worked independently with 5 HandyPEA (Hansatech Ins., Norfolk, England), 1 PEA (Hansatech Ins., Norfolk, England); 1 PAM-2000 (Walz, Effeltrich, Germany), 1 MiniPAM (Walz, Effeltrich, Germany) and 1 OS-1 (Optiscience Corporation, Tyngsboro, MA). Each group replicated 5–10 measurements on each tree. The data analysis took into consideration the behaviour of FV /FM in the six trees sampled, according to the different surveyors. Fig. 2A shows a certain degree of heterogeneity in the results. Unlike the exercise at Passo Pura, here the correlations between surveyors were weak (data not reported). The results of the clusters are shown in Fig. 2B. Two “homogeneous” clusters of surveyors were detectable: one of A together with H, and the other including G, D and B. The others behaved differently. Team “I” was completely different from all the others. A further elaboration concerned the analysis of variance (twoway ANOVA) taking into account the factors “Site” and “Plant”, and their interaction, for each different surveyors. The aim of this analysis was to check if, even if with different results at tree level, the general results were similar. Only the surveyors providing a complete dataset were considered. Based on the “Site” factor, significant differences were found by A, C, D, F, but not by B and G. Based on the “Plant” factor, significant differences were found only by B, C and D. Lastly, the interaction between the two factors was found significant by B, C, D, F and G, but not by A. 4. Conclusive considerations and future directions Intercalibration between teams can be verified easily by measuring the same leaf sample with different instruments. The extreme values F0 and FM proved to be quite variable, but FV /FM , as well as the parameters derived from the normalized curves (for example VJ , VI and those related to them), have proved to be very robust and comparable across different instruments and measurement conditions. The good correlation between instruments, however, suggests the possibility of calculating conversion factors to adjust the results (see example Fig. 1E and F). Lastly, the presence of an instrument that gives strongly divergent results (team “D”, see Fig. 1D) highlights the need for an accurate check, before any measurement campaign, to align or eliminate divergent instruments. Harmonization requires a more complicated approach. The very large differences among surveyors observed in the exercise at Monterotondo Marittimo (Fig. 2) can be explained by the different strategies adopted in choosing the leaf samples within a tree crown (sun vs. shaded leaves; bottom or top of the branches; top or bottom of the crown, cardinal direction of the crown section and so on, see for e.g. Gielen et al., 2007; Sarijeva et al., 2007). The problem of the sampling strategy across a tree crown is discussed also within the pan-european programme for forests health monitoring (Luyssaert et al., 2002). The issues that need to be addressed are: the variability of the leaf responses within the crown, the scientific question 119 to be investigated (assessment of the response in the whole tree, or just in a specific population of leaves assumed as target), as well as the sampling strategy taking in account the spatial heterogeneity of the photosynthesis (Sakai and Akiyama, 2005; Strain et al., 2006). This consideration may appear trivial, but the study of the variability and the sampling strategy are rarely stated in this kind of research. We suggest some (obvious) precautions before starting with a survey, such as (i) to explicitly identify the target population; (ii) to adopt common protocols; (iii) to perform common exercises to prevent bias and misinterpretations. The most important point, however, is likely to be the study of the variability within and between the population(s) considered, in order to estimate the sampling errors and produce robust and statistically sound results. Acknowledgements We would like to deeply thank Prof. Mauro Tretiach (Trieste) for the organization of the exercise held at Passo del Pura. We thank also the Municipality of Monterotondo Marittimo for the logistic support. References Bussotti, F., 2007. Misure ecofisiologiche su piante arboree. Comparabilità e fonti di errore. Sherwood 133, 45–49. Bussotti, F., Cozzi, A., Cenni, E., Bettini, D., Sarti, C., Ferretti, M., 2009. Measurement errors in monitoring tree crown conditions. Journal of Environmental Monitoring 11, 769–773. Bussotti, F., Desotgiu, R., Cascio, C., Pollastrini, M., Gravano, E., Gerosa, G., Marzuoli, R., Nali, C., Lorenzini, G., Salvatori, E., Manes, F., Schaub, M., Strasser, R.J., in this issue. Ozone stress in woody plants assessed with chlorophyll a fluorescence. A critical reassessment of existing data. Ferretti, M., 2009. Quality assurance in ecological monitoring—towards a unifying perspective. Journal of Environmental Monitoring 11, 726–729. Ferretti, M., König, N., Rautio, P., Sase, H., 2009. Quality assurance (QA) in international forest monitoring programmes: activity, problems and perspectives from East Asia and Europe. Annals of Forest Science 66, 403. Gielen, B., Löw, M., Deckmyn, G., Metzger, U., Franck, F., Heerdt, C., Matyssek, R., Valcke, R., Ceulemans, R., 2007. Chronic ozone exposure affects leaf senescence of adult beech trees: a chlorophyll fluorescence approach. Journal of Experimental Botany 58, 785–795. Luyssaert, S., Raitio, H., Vervaeke, P., Mertensb, J., Lustb, N., 2002. Sampling procedure for the foliar analysis of deciduous trees. Journal of Environmental Monitoring 4, 858–864. Paillotin, G., 1976. Capture frequency of excitations and energy transfer between photosynthetic units in the photo system II. Journal of Theoretical Biology 58, 219–235. Sakai, T., Akiyama, T., 2005. Quantifying the spatio-temporal variability of net primary production of the understory species. Sasa senanensis, using multipoint measuring techniques. Agricultural and Forest Meteorology 134, 60– 69. Sarijeva, G., Knapp, M., Lichtenthaler, H.K., 2007. Differences in photosynthetic activity, chlorophyll and carotenoid levels, and in chlorophyll fluorescence parameters in green sun and shade leaves of Ginkgo and Fagus. Journal of Plant Physiology 164, 950–955. Strain, E, Beardall, J., Thomson, R., Roberts, S., Light, B., 2006. Spatio-temporal variability in the photosynthetic characteristics of Zostera tasmanica measured by PAM. Acquatic Botany 85, 21–28. Strasser, A., Tsimilli-Michael, M., Srivastava, A., 2004. Analysis of the fluorescence transient. In: Papageorgiou, G.C., Govindjee (Eds.), Advances in Photosynthesis and Respiration Series. Chlorophyll Fluorescence: A Signature of Photosynthesis. Springer, Dordrecht, NL, pp. 321–362.