Knowledge gap analysis assessing steady-state chlorophyll fluorescence as an indicator of plant stress status

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1 Knowledge gap analysis assessing steady-state chlorophyll fluorescence as an indicator of plant stress status Zbyněk Malenovský, Alexander Ač, Julie Olejníčková Uwe Rascher, Alexander Gallé, Gina Mohammed 22 th April 2014, Paris Esa Workshop

2 Outline of presentation: 1. Relevance of steady-state chlorophyll fluorescence for RS 2. Meta-analysis: 2.1. Collection of studies/data 2.2. Random-effects meta-analysis 2.3. Simple statistics (non-parametric tests) 3. Knowledge-gap analysis 4. Summary

3 1. Relevance of the steady-state chlorophyll fluorescence (F S ) Chlorophyll fluorescence is one of the de-excitation processes during utilization of excessive light in photosynthesis. Courtesy

4 1. Relevance of the steady-state chlorophyll fluorescence (F S ) fluorescence (r. u.) Fluorescence induction kinetics - Kautsky effect Fm Dark adapted plant steady state Fs relevant for remote sensing time [s] Kautsky effect

5 2. Meta analysis 2.1. Collection of studies/data Literature on passive and active F signals recorded under stressful conditions was collected from various platforms (Web of Science, EBSCO, Google Scholar) 3 most important stress factors (water, temperature, nitrogen) were analyzed Stress effect on red, far-red fluorescence and red/far-red ratio

6 2. Meta analysis 2.1. Collection of studies/data Key words: sun-induced, solar-induced F s, steady state, passive Internet database of all reviewed papers: Problems: different units of F s, various functional types of plants, methodology, growing condition, missing information about number of plants and/or measurements, standard deviation Two types of evaluation: META-ANALYSIS (F s measurements, N and STD required) BASIC STATISTIC (based on stress-to-control F s ratio)

7 2. Meta analysis 2.2. Random-effects meta-analysis Statistical tool to compare results of different studies using mean values, standard deviations (variability) and sample size (n). Random-effects model allows for comparison with different effects (i.e. effect is unknown ). Standardized difference in means: Z-value is testing the null hypothesis that the overall mean effect is zero at a given probability level. d = σ 1 2 F s1 F s2 ( n 1 1) +σ 2 2 n 2 1 n 1 + n 2 2 ( ) Significance of the summarized effect in a given group is expressed by the p-value for a one-tailed test.

8 2. Meta analysis 2.3. Basic statistics Measurements of red fluorescence (FR) (688 nm) and far-red fluorescence (FFR) (742 nm) were directly compared without taking into account measurement variability, which allowed to include also studies without variability measures. In order to normalize for different F s units, stress-to-control ratio was computed and compared. The non-parametric Man-Whitney test was applied to test statistically significant differences in stress-to-control ratios with non-gaussian distribution.

9 Standard Difference in Means & Z-values Leaf/ (n=4) p= Meta analysis Results Example of random-effects meta-analysis Leaf/ (n=1) FR Canopy/Pas Canopy/Acti sive ve (n=2) (n=1) p=0.01 Std diff. in means with Std errors 95% confidence interval Z-values (testing null hypothesis) Water stress All studies (n=8) p< Leaf/ (n=6) p< Leaf/ (n=2) p=0.09 Canopy/Acti Canopy/Pas ve (n=3) sive (n=4) p=0.01 p= FR and FFR are systematically decreasing under water stress both on leaf and canopy level. All studies (n=15) p< :00:00 12:00:00 12:00:00 12:00:00 12:00:00 12:00: :00:00 12:00:00 12:00:00 12:00:00 12:00:00 12:00: Standard Difference in Means & Z-values 1 FFR Std diff. in means with Std errors 95% confidence interval Z-values (testing null hypothesis)

10 2. Meta analysis Results Overview of random-effects meta-analyses FR Leaf Leaf Canopy Canopy All FF R Leaf Leaf Canopy Canopy All Water stress N=4 p=0.003 N=2 N=8 p< N=6 p< N=2, p=0.09 N=3, p=0.01 N=4, p= , p< High T stress n< N=4 p=0.6 Low T stress N=4 p=0.9 N=2 n= Both T stress N=5 p=0.2 N=6 p=0.2 N=3, p=0.8 N=4, p=0.6 N stress N=3 p< N=2 p< N=6, p=0.4 n-2, p=0.05 N=4, p=0.9

11 4 3.5 Stress to control chl. fluorescence ratio Leaf/ active (n=8) p=0.0004** 2. Meta analysis Results Example of basic statistic Leaf/ passive (n=3) p=0.6 FR Canopy/ active (n=3) p=0.06 Canopy/ passive (n=4) p=0.3 Water stress All studies (n=18) p=0.0002** :00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00: :00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 00:00:00 12:00:00 Mean with Std errors Median Std deviations 2.5 Stress to control chl. fluorescence ratio Leaf/ active (n=12) FFR Leaf/ Canopy/activ passive e (n=5) (n=5) Canopy/ passive (n=5) Mean with Std errors Median Std deviations Inconsistent results for active vs. passive measurements at leaf level All studies (n=27) p=0.008**

12 2. Meta analysis Results Overview of basic statistics FR Leaf Leaf Canopy Canopy All studies FFR Leaf Leaf Canopy Canopy All studies Water stress N=8 N=3 N=3 N=4 8 2 N=5 N=5 N=5 N=27 Low T stress N=7 N=6 High T stress N=5 N=3 N=5 N=3 Both T stresses 5 4 N stress 5 N=7 N=3 N=26 3 N=6 N=3 N=23 Water stress Low T stress High Tstress Both T stresses FR/ FFR N=6 N=2 N=4 3 N=7 N=6 N=2 5 N stress N=24 N=4 N=4 N=4 N=36

13 2. Meta analysis Results Summary of random-effects and basic statistics Water stress: significant decrease in red and far-red F. Temperature stress: opposite effect of heat (decrease) and chilling (increase) on red and far-red F emissions. Both are decreasing their ratio. Nitrogen stress: effect on F signal is inconsistent. The best indicator is increasing ratio of red to far-red F. Contrasting results for active vs. passive methods (water and nitrogen stress at the leaf and nitrogen stress at the canopy level), without an explanation.

14 3. Knowledge gap analysis Introduction Comparison of actual situation with potential (aimed) future performance. In science, resources are represented by the current stateof-the-art knowledge base, while potentials are new creative inventions advancing the knowledge base and resulting in new societal benefits. Action plan: active steps to bridge the gaps identified between the actual scientific understanding and a potential advancement or a new application research domain.

15 3. Knowledge gap analysis Methodology Gap analyzing questions: 37 Answered: 5 Identified gaps: 32 (22 first order & 10 second order priority) Example of water stress gap analysis questions:

16 3. Knowledge gap analysis Results Confidence in the correct answer ALL STRESS FACTORS ANSWERED high 8% medium to high 5% low 52% NO ANSWER medium 27% low to medium 8%

17 3. Knowledge gap analysis Results WATER STRESS TEMPERATURE STRESS low 42% high 17% high 8% medium to high 15% medium 41% low 54% NITROGEN STRESS medium 23% medium 17% low 58% low to medium 25%

18 4. Summary Results suggest a stress indicative potential of sun-induced F. Sensing of both red and far-red F emissions is recommended. Simultaneous ground measurements are required for validation and to distinguish the stress type. Significant knowledge gaps regarding the utilization of F signal retrieved by space borne remote sensing still exist. Future multi-scale observations and field campaigns are needed to fill the identified gaps and to reduce uncertainty of F interpretation.