Novel Phenospectral Auxanometry using Complexation of ... · Complexation of Optical Spectroscopy and Chromatographic Auxanometry or GC-MS-Auxanometry in Forest Plant Species Vegetation

Corresponding author: Grädow Olle,

chief engineer, INEPCP RAS, Moscow, Russia.

March. 2014 – May. 2014; Section A; Vol.3, No.2, 555-579.

Novel "Phenospectral Auxanometry" using Complexation of Optical Spectroscopy and Chromatographic Auxanometry or GC-MS-Auxanometry in Forest Plant Species Vegetation Phenological Monitoring Based on Gas & Flavor Chemistry Principles with Automatic Pattern Recognition and Analysis

Grädow Olle

IIFS: Journal Impact Factor (2012): 0.801.

E-ISSN: 2278-3229 IJGHC, March 2014 - May-2014; Vol.3, No.2, 555-579.

International Journal of Green and Herbal Chemistry

An International Peer Review E-3 Journal of Sciences

Available online at www.ijghc.com

Section A: Green Chemistry

Research Article CODEN (USA): IJGHAY

555 IJGHC, March 2014 - May-2014; Vol.3, No.2, 555-579.

Novel "Phenospectral Auxanometry" using Complexation of Optical Spectroscopy and Chromatographic Auxanometry or

GC-MS-Auxanometry in Forest Plant Species Vegetation Phenological Monitoring Based on Gas & Flavor Chemistry Principles with Automatic Pattern Recognition and Analysis

Grädow Olle

Institute for Energy Problems of Chemical Physics, RAS

Received: 28 March 2014; Revised: 25 April 2014; Accepted: 06 April 2014

Abstract: In this paper we propose a novel auxanometric system which is fundamentally different from all the previously known analogues, as it allows a simultaneous monitoring of forest plant species growth together with plant development stages. Unlike the quantitative approach in standard auxanometric measurement techniques with the shoot elongation being the only criterion of the plant growth, our system implements a comprehensive qualitative growth dynamics criterion including one-to-one correspondence between the chemical analysis of plant molecular emission and the environmental conditions variations, which allows the feedback analysis between the plant growth / development and the environmental parameter variation. This is provided by the fact that the automatic system during operation in different modes after pattern recognition learning (with the subsequent completion of the database) allows us to investigate and simulate not only a single plant development pattern characteristic of a certain standard feature space, but also to study experimentally the response of the ecological feature structure upon the changing of external factors. The above strategy leads to the emergence of phenological, simulating-biogeographical, biometeorological, bioclimatological and ecology-physiological approaches in auxanometry. The system proposed allows

Novell... Grädow Olle.


parameter variation in the course of the germination / elongation process monitoring as needed (if this is possible within the biotron, climatic chamber or the greenhouse used for germination / elongation of forest plant species), with the following addition of the spectral and chromatographic data in the form of correlation patterns into the database for subsequent comparison. In the case of experimental phelonological spectral plant germination / elongation with the system described it is possible to program and control the temperature using a feedback and thus to predict vegetation initiation accurately. This can be implemented by summing the effective temperatures or their trend detection, which allows to reconstruct the sequence of germination or vegetation of individual plant forms in correlation with the characteristic parameters of the artificial climate (for example, if known that for maple (g. Acer) the sum of effective temperatures is 156.2°С, and for linden (g. Tilia) - 739°С, it is obvious that in the thermal ranking database the linden will be behind the maple). Due to this fact the auxanometric system described allows automatic classification of forest plants in the model plant communities on a set of characteristics according to the phenorhythm types or phenological groups using a more scaled classification than in outdated systems. The same fact allows monitoring in a climate chamber parameter regulation mode by the plant feedback registration using their molecular emission sensors in a controlled physical environment, i.e. the registered parameters from the detector can be considered as a signal changing the chamber operation mode. The system described operates in a wide range of conditions and possesses a number of different application fields, so it is worth being recommended for implementation in both field and laboratory practice of forest engineering. The novel method can also provide phenospectral data ranging useful for establishing the dependence of plant growth and developmental stage phenology on the environmental factors. Since the method is based on combination of several different measurement sources, it is standardless, and thus possesses a wide application in laboratory climatic chambers as well as in natural field conditions with equipment placed in Stevenson screen (meteorological instrument shelter) like the abundant tools for meteo-climatic monitoring.

Keywords: phenospectral monitoring, pheno-spectral library, auxamometer, J. Bose, crescograph, oscillating plate phytograph, mass spectrometry, gas chromatography.

INTRODUCTION

As is known, auxanometry is a classic and standard principle for measuring the growth of tree seedlings. Various auxanometers or their auto-recording variants, auxanographs1, are used for this purpose. The simplest auxanometers were designed in the late 19th century (Barnes 1887, Bumpus 1887, Stone 1892, Golden 1894) and for the first time improved in the first quarter of the 20th century. (Lloyd 1903, Bovie 1912, 1915). The newest designs for that time already offered micrometer-scale resolution and were appropriate for correlation measurements of growth characteristics depending on the changes in the composition of the atmosphere. Since that time (1884), the Österreichische botanische Zeitschrift has repeatedly published a number of reports that mentioned use of the like devices at different institutions or in various publications. (Burgerstein 1884, Fritsch 1905, Nestler

1 Note that the methods utilizing auxanographs are also referred to as auxanometry rather than auxanography, since auxanography in biology refers to the biochemical methods in microbiology associated with the use of auxotrophic mutants when assessing the effects of various preparations or determining optimal growth media for cultivation of microorganisms.



1909). For example, see full "Literatur-Übersicht" in "Österreichische botanische Zeitschrift", 1907. In particular, the second volume of The Physiology of Plants, the classic treatise by Pfeffer, published in 1903 (Fig. 1a), gives the description of a chronographic auxanometer, a high-tech device by the standards of the early 20th century. This construct2, equipped with a self-recoding unit and a dial, remained almost unchanged until the 1950s, when the trend of automating laboratory equipment emerged in Europe and the USA.

However, this trend entered the Soviet practice much later. For comparison, one can see an example of auxanometer from the corresponding entry of the Great Soviet Encyclopedia, released by Macmillan Publishers since 1974 (Fig. 1b). Evidently, its design is almost the same or even more simple as compared with the device by Pfeffer. However, the very same design was reproduced in an unchanged form in the Μεγάλη Σοβιετική Εγκυκλοπαίδεια, since the auxanometers used in the Soviet Union until the last quarter of the 20th century did not actually differ from the foreign prototypes of the beginning of the century. As early as the late 19th century, such devices supplied to Russia or designed there by analogy were equipped with metric dials or scales (see Fig. 1c for an example of such a device with a lower dial scale, borrowed from Vol. 1 of the Brockhaus and Efron Encyclopedic Dictionary3, 1890). Later, most of the auxanometers described in Russian / Soviet papers yielded to the outdated foreign devices in the degree of mechanization and precision (Bovie 1912, 1915). Moreover, these devices all too often were just modified routine auxanometers rather than auxanographs as the gadgets of this kind (as in Fig. 1c) were defined. Several early micrometer-scale auxanometers were fit with a microscope placed in a horizontal position; however, this was metrologically unreasonable relative to simplified variants.

This is because the accuracy in graphical records of the growth dynamics measured with an arc or a recording chart of a drum via an auxanometer needle, similar to a three-arm lever (Engelmann’s), still frequently used in kymographs, widespread in the post-Soviet practice, was far from comparable with the accuracy of micrometer-scale measurements. However, many Russian and Soviet researchers of that time proposed modifications of auxanometers and designed auxanometric devices based on alternative principles. In particular, V.V. Lepeshkin, a Russian emigrant, described in the 1920s the applicability of auxanometry to Thallophytes in his monograph Lehrbuch der Pflanzenphysiologie auf Physikalisch-Chemischer Grundlage (Lepeschkin 1925). At the same time, N.G. Cholodny, a Soviet biologist and a founder of the theory of phytohormonal nature of tropisms, designed an auxanometer able to analyze the role of water (that is, the role of turgor, guttation, and transpiration) in the growth and tropisms of higher plants, which he named a micropotometer (Cholodny 1929) (this device was a key tool in his major experiments clarifying the effects of plant hormones on tropisms). Later, Went, the second contributor to the phytohormonal theory of tropisms, currently known as the Went–Cholodny theory, also took advantage of auxanometry in his later paper of 1933 (Went 1933).

2 Recollect also the Bovie precision auxanometer (Fig. 2a, b) and Harcourt students auxanometer (Fig. 2c) of the 1920s as well as botanical recording lever, still manufactured in the 1930s, which could be used not only in the initial mechanical variant, but also (given an inductance coil or a tube recorder) with a radio electronic recording, which experimenters frequently put to use (Fig. 2d). In India a famous scientist - sir Jagadish Chandra Bose (জগদীশ চ বসু / Jôgodish Chôndro Boshu) also invented a simple device for measuring growth of plants, known as "crescograph" (Fig. 3a) and also as "oscillating plate phytograph" (Fig 3b). 3 A considerably expanded variant of the Brockhaus Enzyklopädie, founded by Friedrich Arnold Brockhaus, German encyclopedist, characterized in the Encyclopaedia Britannica (1911) as “No work of reference has been more useful and successful, or more frequently copied, imitated, and translated, than that known as the Conversations-Lexikon of Brockhaus".



Fig. 1 a Fig. 1 b

Fig. 1 c Fig. 1 d Fig. 1: Outdated auxanometric devices: a - chronographic auxanometer designed by Pfeffer (1903); b - auxanometer / auxanograph construction described in Great Soviet Encyclopedia (1950, 1970); c -

an auxanometer similar to one designed by Pfeffer, d - an arc auxanometer.



Fig. 2 а Fig. 2 b

Fig. 2 c Fig. 2 d Fig. 2: a, b - Bovie Precision Auxanometer and its measuring unit (taken from: Knott, L. E. 1921. L.E. Knott Apparatus Company: Scientific Instruments, Catalogue 26), c - Harcourt Students' Auxanometer (Ibid), d - Botanical Recording Lever (taken from: Palmer, C. F. 1934. Palmer Research and Students'

Apparatus for Physiology, Pharmacology, Psychology, Bacteriology, Phonetics, Botany etc.)



Fig. 3a: Compound lever crescograph made by Dr. J.C. Bose for the measurements of longitudinal plant growth (http://photodivision.gov.in/; Photo Number: 72982).

Fig. 3b: Graphical representation of a precision Bose crescograph: "P - plant; C - clockwork for periodic oscillation of recording smoked glass plate G; S and S' - micrometric screws; K - crank;

R - eccentric; W- rotating wheel" (from the original monograph).



Fig 3c: Oscillating Plate Phytograph as a schematic analogue of crescograph (Jagadish Chandra Bose

Museum, Bose Institute, Kolkata). Free Biswarup Ganguly image from: commons.wikimedia.org.

Note that the design of hydrometric auxanometers has been repeatedly used in irrigation forestry and applied botanical research (Engel & Heimann 1949, Sparks 1958, Kunkel & Gardner 1965, Meyer & Green 1981) for assessing hydration/dehydration and guttation of vegetation forms in wildlife; however, the references to the first paper by Cholodny with rare exceptions are absent. Occasionally, lysimeters were used in the studies of this kind as an environmental control in field auxanometry. Correspondingly, it was possible to speak about “correlation auxanometry”, that is, about transition from detection of cytophysiological mechanisms underlying the growth of individual plant to large-scale ecological climatic and phenological monitoring under field conditions. However, this approach was too laborious for that time before the advent of computer networks for data collection and, correspondingly, was not adopted. Thus, the time scale of auxanometric measurements until the end of the 20th century was also rather limited allowing for monitoring of plant growth only rather than plant development and could not be applied to analyze periodization of plant development as well as phenological phases and growth stages in correlation with environmental conditions.

Meanwhile, a physiological and ecological trend in detecting the responses of growth to particular environmental conditions has been constantly taken into account in the studies involving auxanometry since the 1970s. This was associated with advanced capacities for measurements of this kind owing to available sensors and gauges for recoding physical and chemical parameters. In particular, Rayle and Cleland (Rayle & Cleland 1972) posed the question on (partial) substitution of the auxin functions by the action of hydrogen ions (pH) and carbon dioxide (similar to pH, the concentration of the latter characterizes the redox balance in geobiological systems). Jaffe (Jaffe 1973) analyzed mechanical response to a tribological impact on the seedling growth and the corresponding adaptive compensation; the discovered mechanism was named thigmomorphogenesis. McBride and Evans (McBride & Evans 1977) analyzed and compared direct impacts of acid solution and fusicoccin (a synthetic agent inducing cell plasma acidification by H+ hypersecretion, causing the so-called FC-induced elongation). Further data favored the hypothesis postulating the influence of a hormone-induced pH modification near the cell wall on the growth kinetics (Evans et al. 1980). Then, these results were matched to the discovered function of electrogenic ion pumps (Katou & Ichino 1982);



this suggested that bioelectrogenesis and plant growth and morphogenesis are cognate processes. Using quick motion recording (Mulkey et al. 1983) with time code, the state of a certain physiochemical parameter and each position in seedling growth or tropism were matched in a one-to-one manner. Thus, an integrated pattern of multiparametric growth kinetics for each tree species, variety, or cultivar could be constructed; however, the lack of the necessary hardware and software made it impossible.

In the 1990s, the auxanometers of outdated design were replaced with CCD camera–based (Christian & Lüthen 2000, Steffens & Lüthen 2000) electronic video digitizers with a high time resolution (Evans et al. 1994) and precision mechanoelectrical devices with angular transducers (Claussen et al. 1997). The late 1990s–early 2000s brought forth a most advanced technology—laser interference auxanometry (Budagovskaya & Guliaev 2002, Budagovskaya & Guliaev 2003) (a double laser optical system in auxanometry was for the first time used in 1979 (Taiz & Métraux 1979) at various environmental pH and specific conductivity values, having an osmotic effect on cells). As early as the 1990s, the focus of the tools and systems intended for automated auxanometry shifted towards measuring the plant growth in correlation with environmental conditions (Fernandez & Wagner 1994, Inman-Bamber 1995). The new renaissance in auxanometry driven by computer-assisted image processing (Spalding & Miller 2013) provides one-to-one mapping of the zones responsive to different factors and the zones of different growth intensities in the presence of the corresponding factors, thus framing a new multimodal approach to analysis of growth data.

In particular, this allows for multispectral colorimetric measurements of the plant surface, analysis of Fourier spectra of images for their anisotropy, mapping of absorbed radiation according to isophots, and construction of vector fields for plant growth dynamics using motion compensation technique involving mapping of gradients or pixel to ASCII data conversion. Thus, this new powerful tool can in part replace the biochemical principles, traditionally (1980s–1990s) used for assessing the growth properties. (Evans 1984, Mutaftschiev et al. 1997, Kim & Mulkey 1997), with “nondestructive testing”, functional, and real-time in vivo and in situ monitoring techniques. However, digital imaging is unable to cover the entire critical range of biochemical analysis even if its dynamic and spectral ranges are extended to ultraviolet and infrared regions. This raises concern of conducting one-sided analysis and providing similarly one-sided interpretation of physiological and biochemical / biophysical data due to deficiency in the initial data sources.

Wandering from the subject, note that unimodality and the resulting poor representation were characteristic of the post-Soviet papers on new auxanometry methods. Despite that the first Russian works on electronic auxanometry date to the second half of the 2000s4 (Rassadina et al. 2007), these papers were still rather far from the European and North American works of the early 1990s. The so-called “upgrading” or “renovation” of previous generation devices (for example, a KTP auxanometer

4 This device was designed and produced at the Institute of Biophysics and Cell Engineering, National Academy of Sciences of Belarus, and the Design Office Pribor. However, this is not an auxanometer but rather a phytomonitor, well designed for its time (the end of the last century), intended according to the presentation of its authors for several specialized tasks. Among these tasks are comparative assessment of soil phytotoxicity and fertility; biotesting of natural and waste waters; air pollution assessment; certification tests of plant protection chemicals; control and testing of chemical and bacterial preparations regulating growth and development of agricultural plants; and breeding focused on the search for the traits determining yield, earliness, and resistance to diseases, drought, low temperatures, herbicides, and so on. As for the basic research into the patterns of plant growth, the major purpose of auxanometers, this is the last to be mentioned (http://ibp.org.by/ru/wp-content/uploads/2011/12/electronic-auxanometer.pdf). Therefore, this device is incomparable with standard electronic auxanometers known since the 1970s (Hari, Kanninen, Hari 1978, Courtney, Wilkinson 1978).



by the Laboratory of Experimental Botany of Novgorod University) is of no help, since this trick does not change the physical principles of measurement; frequently, the mere type of recoding is changed, whereas the design remains the same and no new sensors are added. In these Russian studies, they did not even try to simply combine time-lapse imaging principles with analysis of kinetics and signaling mechanisms or further involvement of molecular biological techniques (at that time, typical of this research area abroad (Binder 2007)). Actually, there was a rather dubious difference between the auxanometers sufficient for laboratory training of university students or even schoolchildren interested in biology and analytical devices intended for serious research. Moreover, the bias towards simplifying research equipment was observed rather than designing new advanced tools based on high-precision analytical hardware and their promotion to universities, colleges, and schools. In other words, as they wrote in the 1930s about Lilian J. Clarke, a founder of botany as an experimental science (Clarke 1935), this was “a time when science was still the Cinderella of the curriculum” (W.E.B. 1935). This situation demands a radical change. Otherwise, simplified models using insufficiently representative characteristic criteria will invade the papers utilizing auxanometry as verification reference (W.E.B., 1935).

DESCRIPTION OF THE NOVEL APPROACH

The facts described above demonstrate the need in development of a technology of auxanometry or, more precisely, a new ideology for this approach that would integrate physiological monitoring, analysis of most characteristic biochemical data of “nondestructive testing” (that is, not interfering with experiment by definition), and measurements of growth with the time resolution and duration sufficient for phenological periodization and analysis of stage pattern in plant development. Several aspects should be taken into account when coming up from analysis of growth (auxanometry) to the analysis of development. First, these are phenophases characteristic of individual species. On the scale of forest communities or their laboratory models in climate chambers simulating natural meteorological and climatic conditions, this requires construction of phenological spectra mutually correlated with biochemical spectroscopy/spectrometry data recorded with a time resolution matching the above mentioned integrated approach. This is evident, since the phenological spectrum, demonstrating according to Sukachev and Gams the transitions between phenological phases and the stages from vegetation through flowering eventually to leaf fall, unambiguously corresponds to physiological and biochemical changes in plants at these stages, which can be analyzed by the above mentioned approach. Since phenological spectra are standard data for the forest management units of the CIS and post-Soviet Russia (where they are still retained), it is not a difficult task to integrate these data with geographic reference and geobotanical mapping.

Consider in more detail the potential of the proposed phenospectral auxanometry. As is known, the main plant phenophases are sap flow, leaf emergence, flowering, fruiting and leaf fall. It is evident that (i) the sap flow is accompanied by changes in the transpiration rate and hydrodynamic coefficients of ascending and descending flows; (ii) leaf emergence is accompanied by photosynthetic oxygen emission and change in the transpiration rate caused by an increase in the specific functional surface; (iii) flowering is accompanied by emission of fragrance structures, characteristic from the standpoint of chemical analysis, which depend on the emission phase, that is, on sampling date; (iv) this emission parameter changes when passing to fruiting (although the first fragrance profiles may be recorded at the stage of leaf emergence); and (v) leaf fall is accompanied by emission of leaf decomposition products; and so on. All the products and adducts of physiological emission are chemically detectable by nondestructive (relative to the plant emitting them) assays (atmospheric spectral analysis and environmental gas chromatography).



Thus, what can be recoded by gas chromatography and can be interpreted as characteristic determinants for individual plant developmental stages in model plant communities?

First, evident analytes are gases. Physiological ecology of woody plants (Kozlowski 1990), a cooperative extension of the general physiology of woody plants (Kozlowski & Pallardy 1996), suggests that oxygen emission is not the only chemical criterion for a plant community associated with gases, since the ecological feedbacks that determine physiology of the total emission should be taken into account. In particular, methane emissions from plants of various climatic zones have been recently discovered (Keppler et al. 2006). This induced studies of methanogenic activities of woody plants in many countries. In addition, plants have been shown to emit NO in amounts detectable by gas chromatography (GC) and gas chromatography–mass spectrometry (GC–MS) (Polessakaya 2007). Moreover, the soil–forest interactions should be considered when analyzing the gas emission: an increase in carbon dioxide concentration decreases the plant regulatory effect entailing increased emission of other greenhouse gases (nitrous oxide and methane) from the soil (Davidson et al. 2000, van Groenigen et al. 2011, Knohl & Veldkamp 2011); note that the climatic factors adverse to plants stimulate them to emit greenhouse gases (Qaderi & Reid 2009). Unlike some phytoplankton species (Iglesias-Rodriguez et al. 2008), which increase their yield with carbon dioxide concentration, this is not true for forests. This emphasizes the importance of correctly detected feedbacks in physiological and ecological processes taking place in forest communities (in particular, the proposed phenospectral approach is applicable for this purpose).

Second, another important component is monitoring of transpiration. In part, this correlates with gas dynamic monitoring, since climate convection feedbacks, determining the phases of carbon dioxide content oscillation in the atmosphere, also regulate the air mass temperature, which influences evaporation/plant transpiration from the standpoint of leaf hydraulics (Beerling & Franks 2010, Brodribb & Feild 2010, McKown et al. 2010). The elemental composition of gases emitted by transpiration is assayed by chromatography for both the transpiration streams on the surface and root transpiration in a condensed medium (Monje & Bugbee 1996, Malone et al. 2002, Liao et al. 2006, 2007). The idealization when solving the corresponding model problem in vitro (under air-tight conditions) matches the sample phase and headspace (gas phase) in terms of gas chromatography, where the atmosphere or experimental medium of a climate chamber is regarded as a headspace and liquid transpiration surface, as a sample phase (by analogy to (Kolb & Ettre 2006)). Thus, an air-tight climate chamber connected to channels of a gas chromatograph and its system for sampling volatile compounds may represent a time-resolved analog of the static headspace–gas chromatography assay in a native environment. Thus, transpiration and changes in the gas composition of standardized medium in a climate chamber can be concurrently monitored to deduce the main exchange processes between the plant biomass and simulated environment from the standpoint of physiological ecology of woody plants (Kozlowski 1990).

Third, gas chromatography5, including GC–MS and the mentioned headspace method, is applicable to analyze the odor volatiles emitted by woody plants and identify them according to the corresponding databases (Jennings 1980, Heydanek & McGorrin 1981, Werkhoff et al. 1998). As is known, woody plants and the phytocommunities of forest layers emit characteristic odor volatiles in amounts sufficient for their organoleptic detection at flowering and fruiting stages. (For example, see Tucker A.O., DeBaggio, 2009, and also "Handbook of Fruit and Vegetable Flavors" (Ed. by Y. H.

5 Sorbent tubes for sampling volatile organic compounds and a thermal desorber are most appropriate for this purpose when using a field and laboratory emission cell (FLEC) or the input from a climate chamber in the case of laboratory realization.



Hui, 2010), "Flavor and Health Benefits of Small Fruits" (Ed. by M. Qian, A. Rimando, 2010)). In addition, trees frequently emit phytoncides (Li et al. 2006), routinely detectable by gas chromatography (Dmitriev et al. 1983). The emitted phytoncide activity is maximal during the light phase of photosynthesis and minimal during the dark phase. Note that the intensity of phytoncide generation correlates with respiration intensity, air temperature, and so on. Note also when applying the proposed method to forest practice that the highest effect of phytoncide activity is characteristic of forests rather than urban plantations (Li et al. 2008 a, b).

It is possible to use rather large climatic chambers when assaying volatile organic compounds (VOCs), since the phytoncides spread their action to 3–5 m (estimated according to inhibition of microorganisms). Current methods for odor analysis and bioprocessing (see, for example: "Flavor, Fragrance, and Odor Analysis" (Ed. by R. Marsili, 2011) and "Flavours and Fragrances: Chemistry, Bioprocessing and Sustainability" (Ed. by R.G. Berger, 2010)) are sensitive enough to detect fine compositions not only by definitions of dictionary entries (De Rovira 2004), but also in a precise analytical6 sense due to the advance in automated odor quantification technologies (Wise et al. 2000). In particular, electronic nose detection (Persaud & Dodd 1982, Röck et al. 2008, Degenhardt et al. 2012, Jin et al. 2012), constantly improved and already used in several companies, makes it possible to do without the human perception in classification of odors determined by automated devices.

The need in longitude time-resolved (phenological) odor analysis is explainable by that (i) microclimatic and phenological factors influence the chemical composition of emitted compounds (Lago et al. 2006); (ii) semiochemical characteristics of the plant signals to insect pollinators and of functionally opposite chemical repellents change with time (Schwarz et al. 2009); and (iii) palynological patterns of flowering seasons for individual plant species depend on climatic and geographic parameters determining phenology of a particular species or community; consequently, their odor characteristics change synchronously to or in a correlated manner with geographic zonal change in the flowering dates (Puc & Kasprzyk 2013).

Thus, automated (not subjectively organoleptic but a correct chromatography-based flavor chemistry assay) analysis of plant odor sources is absolutely necessary. Gas chromato-mass spectrometry (GC–MS) is frequently used for studying VOC emission from the leaf surface (Fredrickson et al. 2007); this suggests expanding mere gas chromatography detection7 by equipping a chromato-auxanometer device with a mass spectrometer, thereby transforming chromato-auxanometry into chromato-mass auxanometry. Тhere is also a technique known as desorption electrospray ionization (DESI) which allows to perform blooming plant analysis and distinction according to direct mass-spectra, operating in the framework of flavor chemistry (Fig. 4).The phenology of plant forms depends on carbon fluxes, humidity, diurnal temperature range, and surface energy balance (Hanes 2014). This suggests that the databases of the control software for the proposed device should match the chromatography or chromato-mass spectrometry data and the measured environmental parameters at each time moment

6 This is possible owing to specialized libraries in a format of computer databases suitable for both GC-FID and GC–MS data. Examples are Shimadzu FFNSC library (the GC data for it were provided by Prof. Mondello group, University of Messina, Italy) and GC/FID & GC/MS RTL Flavor Databases by Agilent Technologies (utilizing GC/MSD ChemStation G1701CA software with RTL and Screener;

http://www.chem.agilent.com/Library/slidepresentation/Public/Flavors_RTL_Databases.pdf). 7 Although most biochemical samples and agents belong to soft condensed matter, gas biochemical studies should not be regarded in the proposed method as an exotic tool: as long ago as the 1980s, such studies were performed and meetings in this field were organized (in particular, the Symposium on Gas Enzymology - see all materials of that symposium in: "Gas Enzymology" Symp. Proc. at Odense University, Denmark-1984 (Ed. by: H. Degn, R.P. Cox, H. Toftlund). Kluwer Acad. Pub., Dordrecht, 1985. 264 p).



of monitoring. Note that microfauna also influences many of these parameters, since the gas exchange in photosynthesis (Geider 1992) is opposite to the gas exchange in respiration of microfauna (Maina 1998), which cannot but influence their interactions in terms of physiological ecology (Kozlowski 1990).

a)

b) Fig. 4: DESI-analysis and mass-spectrometric chemical distinction of blooming flowers (photo taken

from http://www.sciencedaily.com/ & http://news.sciencemag.org/)

Therefore, it is necessary to either guarantee the absence of microfauna (invertebrates) in soil and on plants (as a quarantine of seedlings before experiments or monitoring) or to somehow take this into account in a mathematical model describing the effects of their molecular emission on equilibrium of the system using multivariable feedbacks (Skogestad & Postlethwaite 2005). All these examples of abiotic and biotic factors interfering with monitoring of the described local forestation model are intended to demonstrate that the aerochemistry of this system is complex. This necessarily entails the need in additional methods for data analysis and collection. As a rule, a computer-aided identification of organic compounds utilizes a set of methods rather than a single technique and determines both qualitative and quantitative specific features of an analyte converting them into various molecular descriptors (Todeschini & Consonni 2009) according to the characteristics measurable with the used tools. Good practice recommends involving chromatography and mass spectrometry in addition to



spectroscopy in computer identification of analytes using databases (Vershinin et al. 2002). According to the proposed ideology for designing/programming the monitoring device, the system will analyze its internal state over all sources of the signal to send real-time signals, be regulated, and allow for defining complex parametrics.

Fig. 4 c: Portable mass spectrometer for field conditions. R.G. Cooks and his apparatus.

(For example, see also: Anal. Chem, 2014, 86 (6), pp. 2900–2908. DOI: 10.1021/ac403765x).

Thus, we actually speak about development of an expert system (Hemmer 2007) with the functions of multisensor data fusion (Hall & McMullen 2004, Raol 2009, Mitchell 2010) and integrated identification, corresponding to signal collection from many sources. Since historically (starting from the 1930s), auxanometers were combined with monochromators or monochromator spectrometers8 (1930), it is logical to supplement gas chromatography with at least optical spectral analysis (which compiles with (Vershinin et al. 2002). First, from the standpoint of nonlinear physics of ecosystems (Meron 2013), the spectral peak dynamics during vegetation and transitions between states (alternation of phenophases) is nonlinear, corresponding to biophysical kinetics of this process. Second, most of the self-consistent models for forest dynamics (Botkin 1993, Buongiorno et al. 2003, Pretzsch 2010) are nonlinear and involve the corresponding feedbacks.

These two facts suggest that identification properties of the experimental chamber interior as a dynamic system (Isermann & Münchhof 2011) are interpretable as parameters of complex system dynamics (Giantomassi 2012) for nonlinear identification (Nelles 2001) in complex networks (Barrat et al. 2012), which are actually the ecological systems in terms of multisensor information (in particular, in remote sensing systems (Lee et al. 2013)). In terms of the mixed methods approaches (MMA; a combination of different methods aimed at verification and assessment of falsifiability of each method and the overall data array obtained using these methods) (Creswell 2013), only matching of gas chromatography, optical spectroscopy, and climate monitoring data can give a

8 This may be also combined with laser auxanometry, utilizing monochromatic (by definition) laser radiation (Budagovskaya & Guliaev 2002, 2003)



sufficiently high-quality and unambiguous pattern of the processes in the proposed device at different levels of endo- and exoecological connections. This interferes with a systematic identification and fingerprinting of individual species phenophases unless controlled environmental conditions are provided. As for gas chromatography, an intricate mathematical apparatus involving multivariate methods (Cserhati 2008) is used for tackling complex problems irresolvable within routine approaches. Note that multivariate chemometrics in terms of QSAR (quantitative structure–activity relationships) approach can be matched to the methods determining structural correlations of individual biochemical properties (Mager 1988).

This is important for the ideology underlying design of the experimental facility type considered here, since QSAR methods are widely applied in simulating the environmental response to external impacts (Nendza 1998). The computer methods selecting the response according to physical properties of molecular agents had been long ago designed (Bumble 1999). Such an approach is also appropriate for inverse problems; therefore, it can be used for modeling in monitoring of molecular emission of forest plants and their communities (for example, in the described monitoring facility or the chambers of biotron or phytotron types with the sampling system similar to the described one)9. Typically, multimodal and multiparametric remote sensing systems are used in combination with geographic information systems (GIS) for global monitoring (Rodriguez-Bachiller & Glasson 2004).

However, the approximation of global dynamics (with interpolation for relatively local regions) for local phytophysiological monitoring of forest systems may be in part replaced by phytotron–based monitoring with multiparametric recording and variation of experimental environmental conditions in climate chambers/facilities similar in their design to the chambers proposed in this paper (even to testing a winter recording of vital parameters during a frost-resistant germination of forest species (Warnock 2013)) creating the artificial microclimate simulating particular geographic regions, which induces the corresponding quantifiable phytophysiological responses (Jones 1992) 10.

As a rule, hyperspectral imaging (for example, see: "Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests" (Ed. by M. Kalacska, G. A. Sanchez-Azofeifa, 2008) and "Hyperspectral Remote Sensing of Vegetation" (Ed. by P. S. Thenkabail, J. G. Lyon, A. Huete, 2011)) is used in multiparametric remote sensing systems for mapping vegetation as well as lidar-based methods, in particular, laser lidar devices (Helt 2005) (to say nothing of resource-intensive methods involving interferometry and polarimetry (Lavalle 2012)). Therefore, it is reasonable to combine the monochromators or scanning laser equipment in chromato-auxanometry devices with spectral mapping of plant surface to better match the GIS prototype. This problem is in no way redundant, since it is known that spectral reflectance characteristics may vary even within individual plant. This may be caused by

9 Note that QSAR has been widely used in automated interpretation of gas chromatography data for aromatic compounds (Jönsson et al. 2008, Drosos et al. 2010) and the constituents of plant aroma compositions (Du et al. 2008, Tan & Siebert 2008). That is why the use of QSAR approach in flavor chemical fingerprinting of forest ecosystems and their models has solid grounds. On the other hand, quantum chemical descriptors for GLPC have been theoretically grounded (Hoffmann et al., 2009), allowing QSAR principles to be also applied to simple gases involved in photosynthesis and respiration. 10 This looks especially fitting for modeling of the forest management in the Russian Federation for the following reasons. According to the Köppen climate classification (Kottek et al. 2006, Peel 2007), the major part of the Russian Federation belongs to Dw and Df subarctic zones with prevalence of Dfc and Dfd (here, c stands for the lower temperature range of –25 to –10°C and d, for the range of –40 to –25°C, which some notations interpret as painfully cold).



(i) Different accessibility of photosynthetically active radiation to a leaf and the corresponding change in pigment concentration (Gitelson et al. 2003);

(ii) Changes in the leaf pigment composition during plastid biogenesis from proplastids through mature forms to gerontoplasts (Biswal 2003)

(iii) stress-induced effects on a leaf (Mohammed et al. 2000) (as mentioned above, this changes the gas molecular emission by plants);

(iv) Change in the plant water content and the ratio of water to oxidized species (Dasgupta 2009);

(v) Gradient in chlorophyll content (Chen & Chen 2008);

(vi) Variation in the plant developmental forms and the corresponding differences in a leaf structure (Sims & Gamon 2002) to eventual changes in dead leaves (for example, see: Plant Cell Death Processes. Ed. by L. D. Nooden, 2003).

All these processes are accompanied by gas molecular emission to the degree that makes it possible to match the leaf reflectance spectra and emission chromatograms or mass spectra recorded online simultaneously with the former using a multichannel system. Spectral mapping can be performed in a real-time streamline mode using cooled (as an ideal case; not obligatory) digital cameras and subsequent mathematical processing involving parallel data classification using mainframe computers (Chang 2003, 2013) and 3D visualization (Kim 2002).

The STATA/MP software package (Becketti 2013) is appropriate for processing the data recorded using an auxanometer-based device utilizing physical measurement principles with the help of multiprocessor/multicore computers. This software package allows not only for processing of time series data, but also for maximum likelihood estimation (Gould e al. 2010), multilevel modeling corresponding to multidimensional strategy of data collection (Rabe-Hesketh & Skrondal 2012 a,b), as well as the cause and effect clarification using structural equations (Acock 2013) to say nothing of conventional regression analysis (Kohler & Kreuter 2012).

Thus, the problems of spectral pattern recognition (Siddiqui et al. 1999) (in particular, IR spectra (Zachor 1983), since multispectral/hyperspectral remote sensing in IR range is available (Vollmer & Möllmann 2010) to reflect the thermal leaf properties in a climate chamber) solved by computer processing of spectral monitoring data as discovery of characteristic patterns in terms of bioinformatics (Parida 2007) are supplemented in the case of hyperspectral sensing with multispectral pattern recognition (in particular, see "Multispectral Image Processing and Pattern Recognition (Ed. by J. Shen, P. S. P. Wang, T. Zhang, 2001)) in all spectral ranges as, for example, in terms of the ISODATA algorithm (Ball & Hall 1965). Moreover, when considering the processes in dynamics, this is supplemented with a new level of spectral data, namely, the product of a window-based spectral processing of variation in phytophysiological parameters in the time cycles determined by circadian and phenological rhythms (possibly, see: "Spectral Theory And Nonlinear Analysis With Applications to Spatial Ecology" (Ed. by Cano-Сasanova S., Lopez-Gomez J., Mora-Сorral C., 2005).

In this case, in addition to the optical and other data recorded by remote sensing of vegetation based on standard physical principles when a mobile detector cannot target individual flora elements during monitoring (Jones & Vaughan 2010), stationary detectors of an auxanometer have an additional advantage of analyzing phenological and phenospectral dynamics of these factors and indicators of



physiological response (this dynamics is actually the object of nonlinear ecological analysis - see above), in particular, in the physiological ecology of woody plants (Kozlowski 1990)). Thus, it is evident that combination of optical, chromatographic and multispectral image information requires that the hybrid device has considerable computing power. However, a comprehensive computer-aided processing of this type is fully justified by high predictive properties of the constructed model applicable to pragmatic forest management. In other words, instead of auxanometry as monitoring of the “yesterday’s” growth coefficients (which can be also determined via conformal transformations (Gradov, Notchenko 2012) of a forest plantlet/seedling, we obtain a high-quality identification of the phases constituting the development and a multiparametric equivalent model with predictive capability (over a limited time period), in particular, for programmed changes in parametrizable environmental characteristics.

CONCLUSIONS

Thus, we propose an auxanometry-based integrated device that

(1) Makes it possible to monitor the primary growth of forest tree species in developmental perspective owing to the use of an intergraded qualitative criterion as the indicator of growth dynamics rather than a quantitative criterion (as in routine auxanometry, implying an increase in the seedling length as a sole criterion for growth). The integrated qualitative criterion is formed based on a one-to-one matching of the analytical chemistry data on plant molecular emission and variation in environmental characteristics, which allows for analysis of the feedbacks between plant growth/development and changes in environmental parametrics;

(2) In the course of operation in various modes with training to pattern recognition accompanied by database updating, the proposed device allows not only for studying and modeling of only plant developmental pattern characteristic for a certain standard space of traits, but also for assessment of experimental response of ecological trait structure to changes in environmental parameters. This means the possibility to use phenological, simulative biogeographic, biometeorological, bioclimatologic and ecological–physiological approaches according to the research need (if allowed by the design of a biotron, climate chamber or a greenhouse used for germinating forest tree seedlings) compiling the corresponding spectral and chromatographic data as correlation patterns in databases for further use;

(3) In the case of a phenospectral experimental germination, it makes possible to program the temperature mode and regulate it via a feedback, providing a precise prediction for the date of vegetation onset based on summing of the effective temperatures or detecting their trend. This allows for reconstruction of the order of germination or vegetation of individual plant forms in correlation with characteristic parameters of artificial climate (for example, it is known that the sum of effective temperatures required for the maple Acer is 156.2°C and for the linden Tilia is 739°C; correspondingly, the linden in temperature ranging in a database will be placed later than the maple);

(4) Automatically classifies the woody plants in model phytocommunities according to a set of characteristics into phenorhythm types or phenological groups using a more detailed



scale as compared with the earlier used gradation of phenorhythm types, comprising only two evident types of evergreen and defoliating trees;

(5) Provides the regulation of climate chamber parameters via recording the feedback from plants using detectors and sensors for their molecular emission in a controllable physical environment, i.e., the parameters recorded by the sensor part of the integrated climate chamber themselves are the signal for changing its operation mode.

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Corresponding author: Grädow Olle, chief engineer, INEPCP RAS, Moscow, Russia

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