shoots have a highly predictable relationship between stem mass and leaf mass (circles). Leaf harvesting temporarily diverts this relationship (diamonds), but plants stripped of their leaves sooner or later recover the pre-damage leaf mass vs. stem mass relationship (arrows). (B–D) Examples of "stressful" environments. (B) Possible variants at different elevations: in Pinus cembra from below (circles) vs. above (triangles) 1800 m a.s.l., the scaling of leaf mass vs. total body mass (roots included) follows the same exponent (∼0.85), but leaf mass per unit of body mass (i.e., Y intercept) is higher in high elevation trees. (C) Boxplot of annual shoot growth and needle length between wet ("favorable") and dry ("stressful") sites in Pinus sylvestris (dashed and solid lines are mean and median values respectively). This approach seems to show categorical differences between trees in sites that could be arbitrarily classified as stressed and unstressed. (D) However, when the same samples of Figure 2C are plotted as part of an allometric series, it is clear that the scaling of leaf mass vs. shoot mass (the last three growing years) converges on the same trajectory in both wet (circles) and dry (triangles) conditions. This result highlights that the species is able to build similar allometries of the distal parts of the plant in spite of different environments.

leaves per unit of body mass) are possible under the same scaling relationship (i.e., the relative co-variation of both traits). This example shows that our approach highlights important biological questions masked by the traditional categorical approach.

Our second example comes from Scots pine (Pinus sylvestris) trees (**Figures 2C,D**), which grow tall on deep soils but are short and thin, on shallow, rocky soils. In terms of traditional value laden terminology, the "stunted" trees on rocky soils are often described as growing in "stressful" conditions, as reflected by their much lower annual length growth increments and shorter needles as compared to taller trees on moist, deep soils (**Figure 2C**). Our allometric approach highlights that the often value-laden terminology of "stress" in fact hides much valuable biological insight, even leading to very different conclusions. Plotting nearly the same variables against one another (**Figure 2D**) shows that, rather than two distinct categories, the plants considered "stressed" and "unstressed" are in fact indistinguishable with regard to their patterns of trait covariation. In this example, needle mass scales with stem biomass in exactly the same way in "stressed" and "unstressed" plants. This result highlights an entirely different set of biological issues as opposed to the traditional categorical, value-laden approach. Whereas the categorical approach highlights limitations of growth on dry sites as opposed to imaginary optima, the allometric approach instead shows that the plants in both situations are constructing the distal part of the stem (that bears the needles) along essentially identical allometric scaling relationships, though of different sizes.

These examples illustrate how, from an allometric perspective, the notion of "stress" is largely an inheritance from forestry and agriculture, in which any factor that reduces yield is described with negative terminology (Körner, 2003). However, from an evolutionary point of view, it is hard to see how the small trees of dry sites can be classified as "suboptimal," "limited," or "stressed." Instead, their small stature likely represents an adaptive response to prevailing conditions. That they scale similarly to their larger conspecifics on deep, moist soil in their trait relationships gives no reason to consider them as "stressed," an observation that the traditional categorical approach conceals.

#### CONCLUSION

Our quantitative approach does not require arbitrary categorizations of "stress," because it involves testing the prediction that distance from the general allometric slope should be associated with differences in performance (**Figure 1**). From this point of view, the dividing area between adaptive differences, which should maximize performance in the relevant environment, and those that push individuals beyond their zones of optimal performance, should be explorable (cf. Ellison and Jasienska, 2007). This exploration is not helped, and indeed is often hindered by, use of the term "stress." For plant ecologists and evolutionary biologists interested in discovering how the plant form-function relationship has evolved, valueladen conceptions of "stress" can be replaced by biologically rich methodological approaches such as those shown in **Figure 2B**. Clearly, the plants in "stressed" (high altitude) habitats grow slowly. Study of vital proportionalities between parts from the point of view that we suggest, however, reveals that these

#### REFERENCES

plants are probably well acclimated to the local conditions (**Figure 2C**). Use of the term "stress" only masks this adaptive adjustment. Whatever the causes of variation in allometric slopes or intercepts, the allometric perspective we describe here offers a means for thinking about "stress" in quantitative terms. This framework will allow exploring simultaneously adaptation, acclimation, and "stress" in plants in a quantitative way beyond artificial categorization of these concepts (see Lortie et al., 2004), and will thus serve as a basis for testing a wide array of hypotheses regarding plant performance and fitness.

#### AUTHOR CONTRIBUTIONS

TA, GP, and MO developed the idea, provided the main experimental data, wrote the first draft of the manuscript and revised the text, SL provided additional experimental data, FS and SL interpreted the data and intensively discussed and revised the text.

#### ACKNOWLEDGMENTS

The Authors warmly thank Claudio Fior for providing the data of allometry in Pinus cembra (**Figure 2B**). This work was promoted and supported by the EU COST Action FP1106 (STReSS). The Authors are profoundly indebted with Ute Sass Klaassen who has energetically encouraged the discussion among the COST delegates. Collection of data from Moringa was possible thanks to project IT200515 of the Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica, UNAM.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Anfodillo, Petit, Sterck, Lechthaler and Olson. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Meteorological Drivers of Extremes in Daily Stem Radius Variations of Beech, Oak, and Pine in Northeastern Germany: An Event Coincidence Analysis

Jonatan F. Siegmund1, 2 \*, Tanja G. M. Sanders 3 , Ingo Heinrich4 , Ernst van der Maaten5 , Sonia Simard4 , Gerhard Helle4 and Reik V. Donner 1

#### Edited by:

Cristina Nabais, University of Coimbra, Portugal

#### Reviewed by:

Walter Oberhuber, University of Innsbruck, Austria Alicia Forner Sales, Museo Nacional de Ciencias Naturales-CSIC, Spain

> \*Correspondence: Jonatan F. Siegmund<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

Received: 27 February 2016 Accepted: 12 May 2016 Published: 03 June 2016

#### Citation:

Siegmund JF, Sanders TGM, Heinrich I, van der Maaten E, Simard S, Helle G and Donner RV (2016) Meteorological Drivers of Extremes in Daily Stem Radius Variations of Beech, Oak, and Pine in Northeastern Germany: An Event Coincidence Analysis. Front. Plant Sci. 7:733. doi: 10.3389/fpls.2016.00733 1 Research Domain IV—Transdisciplinary Concepts and Methods, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 2 Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany, 3 Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 4 Department 5 Geoarchives, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany, 5 Institute of Botany and Landscape Ecology, University of Greifswald, Greifswald, Germany

Observed recent and expected future increases in frequency and intensity of climatic extremes in central Europe may pose critical challenges for domestic tree species. Continuous dendrometer recordings provide a valuable source of information on tree stem radius variations, offering the possibility to study a tree's response to environmental influences at a high temporal resolution. In this study, we analyze stem radius variations (SRV) of three domestic tree species (beech, oak, and pine) from 2012 to 2014. We use the novel statistical approach of event coincidence analysis (ECA) to investigate the simultaneous occurrence of extreme daily weather conditions and extreme SRVs, where extremes are defined with respect to the common values at a given phase of the annual growth period. Besides defining extreme events based on individual meteorological variables, we additionally introduce conditional and joint ECA as new multivariate extensions of the original methodology and apply them for testing 105 different combinations of variables regarding their impact on SRV extremes. Our results reveal a strong susceptibility of all three species to the extremes of several meteorological variables. Yet, the inter-species differences regarding their response to the meteorological extremes are comparatively low. The obtained results provide a thorough extension of previous correlation-based studies by emphasizing on the timings of climatic extremes only. We suggest that the employed methodological approach should be further promoted in forest research regarding the investigation of tree responses to changing environmental conditions.

Keywords: dendrometer measurements, event coincidence analysis, climate extremes, growth response

## 1. INTRODUCTION

During the past 15 years the systematic installation and operation of dendrometers and analysis of the obtained data has received increasing interest in forestry sciences. While the first attempts (Friedrichs, 1897) to use dendrometer data to analyze tree response to environmental conditions were clearly limited by the technical conditions of early instruments, recent developments in the production of modern automated highprecision dendrometers offer the ability to generate dendrometer time series at very high temporal and spatial resolution (Drew and Downes, 2009). The detailed representations of activity in the tree stem—shrinkage, recovery, and swelling cycles—allow for a high-temporal investigation of the tree stem as well as long-term morphological and short-term physiological dynamics (Zweifel and Häsler, 2001; Deslauriers et al., 2003; McLaughlin et al., 2003; Bouriaud et al., 2005; Daudet et al., 2005; Vieira et al., 2013). Where additional environmental information is available, dendrometer data can provide information on the tree stems response to external factors, especially meteorological conditions (McLaughlin et al., 2003; Denneler et al., 2010; Miralles-Crespo et al., 2010; Oberhuber and Gruber, 2010; Jezik et al., 2011; Butt et al., 2014). Investigations such as these are also important in order to better understand the diurnal cycle of sap flow and leaf water potential (Drew and Downes, 2009).

Beyond the fundamental understanding of tree functioning, dendrometer data can also indirectly provide important information on the carbon cycle at the local, regional or global level. Even though stem radius does not allow estimates of total cell numbers, it is an important proxy for a forest's above ground biomass (Schulte-Bisping et al., 2012), because it can help to determine the wood volume available for the fixation of carbon (Cuny et al., 2015).

To the authors' best knowledge, almost all above-mentioned studies have investigated dendrometer data using classical statistical tools like linear correlation analysis or linear multiple regression. These powerful methodological approaches have led to an understanding of the relationship between stem size changes and various environmental parameters. Yet, correlationbased approaches generally take all parts of the distributions of two variables of interest into account and therefore describe the joint behavior of these variables. A crucial question only sporadically addressed so far is how tree stem radius variations (SRVs) are linked to extreme weather conditions. This question gains special importance, since recent climate projections suggest a rising frequency and severity of meteorological extreme events for many parts of the world (Barriopedro et al., 2011; IPCC, 2013; Petoukhov et al., 2013). Consequently, analyzing the impact of such extremes on tree SRVs, on the one hand can help to better understand an event's impact on tree functionality and carbon cycle, and on the other hand, because different tree species may be impacted differently, to generate recommendations for future forest management (Spathelf et al., 2014). New findings concerning the response of stem radius to extreme meteorological conditions would also help to improve existing climate-growth-models like TREERING (Fritts et al., 1999) or CAMBIUM (Drew, 2007).

An important study addressing the response of stem-size fluctuations and tree radius growth to climatic extremes using a large number of dendrometer data sets was recently published by Butt et al. (2014). However, this study did not report explicit results regarding the response of the tree stem to extreme meteorological conditions, due to the fact that they have only used ordinary linear regression to analyze the data.

In this study, we employ the novel methodological approach of event coincidence analysis (ECA) to quantify possible simultaneities between extraordinary daily stem variations and extraordinary meteorological conditions. Here, the commonly used term extreme is replaced by extraordinary, referring to the upper and lower tails of the empirical distributions of the variables of interest. Due to the comparatively short investigation period of 3 years (and only 8 years of climatological data), "real" extreme events are difficult to identify or define and therefore the investigation of extraordinary conditions shall represent a tree's reaction to the tails of the empirical distribution of weather events, which may well be exceeded by future developments under climate change. Therefore, conclusions from this study's results on trees' reaction on weather extremes should be made unter consideration of the used definition of "extraordinary events."

Taking into account the existing literature on SRVs and their relation to meteorological conditions we expect that extraordinary climatic events, specifically temperature events, and extraordinary dendrometer variations should occur simultaneously. Additionally, it can be expected that there are clear inter-species differences concerning the reaction to extraordinary meteorological events.

## 2. MATERIALS AND METHODS

#### 2.1. Study Area and Data Sampling 2.1.1. Study Area

The study site was close to Lake Hinnensee (53.33◦N, 13.19◦E) in the northeastern part of Germany. The site is located within the Müritz National Park. Large parts of this protected area have been classified as UNESCO World Natural Heritage in 2011. The park is characterized by 200–300 years old mixed beech, pine and oak stands. The climate is semi-continental, typical for central Europe, with a mean annual temperature of about 8◦C and an annual precipitation between 550 and 650 mm. The soil at the study site as well as at the meteorological station (see Section 2.2.2) is a brunic arenosol on sand of outwash plains, characterized by strong hydraulic permeability Müller (2014).

#### 2.1.2. Data Sampling

The dendrometer data were collected for three tree species: European beech (Fagus sylvatica), Scots pine (Pinus sylvestris), and Sessile oak (Quercus petraea). The distances between the individual trees range from ca. 4 to 20 m and the selection of trees was based on the canopy status of the individual trees (i.e., only dominant trees or co-dominant trees were equipped with dendrometers). This study focuses on the species' response to meteorological conditions, hence the dendrometer data are not differentiated according to the relative positions of the individual trees in landscape (see Section 2.3), but trees were selected along a transect from the lake shore to a terrace ∼15 m above Lake Hinnensee, in order to sample a possibly large variation of individual local stand variations. The equipped trees have an average height of 26 m with diameters between 50 and 70 cm at breast height.

For each tree species, 10 individuals were equipped with Ecomatik DR point radius dendrometers (Ecomatik GmbH, 2015) installed at 1.2 m height. The sensors were installed at the north face of the trunks in order to avoid direct irradiation. They have a temperature coefficient <0.1m/K. Bark was mostly removed from pine and oak trees prior to setup. SRVs were measured at a temporal resolution of 30 min over a 3-years period between 2012 and 2014.

## 2.2. Data Preprocessing

#### 2.2.1. Dendrometer Data

After a comprehensive quality check, the raw dendrometer data were pre-processed using the following three steps:

during which only a few strong positive precipitation events were observed.

#### 2.2.2. Meteorological Data

In order to define days with extraordinary meteorological conditions, data from a nearby meteorological station in Serrahn (at a distance of less than 2 km from the study site) were used. The soil conditions at the dendrometer site and the meteorological station site are comparable, but not identical. Systematically differing soil temperatures, for example, can not be excluded. In addition to air temperature and precipitation, the station provides information on (relative air) humidity, soil temperature, air pressure and incoming solar radiation. The data set is available starting January 2006 at a temporal resolution of 1 h. Similar to the daily SRVs, the meteorological data were pre-processed in order to identify events of extraordinary daily meteorological conditions:

The following meteorological variables were used: air temperature at 2 m (Tmin, Tmean, and Tmax), land surface air temperature at 5 cm (LSTmin, LSTmean, and LSTmax), soil temperature in 20 cm depth (STmin, STmean, STmax), relative humidity (rHmin, rHmean, rHmax), total precipitation (Ptot) and incoming solar radiation (RADmean and RADmax). Many of these variables are highly correlated among each other. Hence, for a study using classical statistical approaches a principal component analysis (as performed by, e.g., Beck et al., 2013) would be appropriate in order to reduce the number of meteorological observables (i.e., to eliminate collinear variables). However, in this study the novel statistical approach of ECA (see Section 2.3) is applied, where the reduction of dimensions based on their common mean behavior (correlation) would not be useful, since the information of interest (timing of extraordinary events) could eventually get lost. Additionally, this study is particularly focused on the different variations of air, surface and soil temperatures, for example. As an alternative, a dimensionality reduction based on event coincidence rates (see Section 2.3) replacing classical linear correlations as similarity measure could be performed as a preparatory step. The utilization of such a novel approach is, however, beyond the scope of the present study.

#### 2.3. Statistical Analysis

#### 2.3.1. Bivariate Event Coincidence Analysis

In order to investigate the simultaneity of events in meteorological variables and SRV, we apply event coincidence analysis (ECA) (Donges et al., 2016), a novel yet conceptually simple statistical concept. In its basic setting, ECA considers two sequences of events of different types (A and B). As the hypothesis to be tested, events of type B are considered to causally influence the timing of events of type A. To cope with realistic scenarios, ECA allows to not only trivially quantify the number of exactly simultaneous occurrences of events of both types, but to consider also lagged as well as time-uncertain responses. For the latter purpose, a time lag parameter τ as well a temporal tolerance window 1T can be additionally taken into account. Then, ECA counts how often events of types A and B occur with a mutual delay τ in both sequences within a certain temporal tolerance 1T. The resulting number of "event coincidences," divided by the total number of events in one of the series is called the coincidence rate r.

Since the statistical analysis described above is not symmetric, ECA defines two distinct types of coincidence rates, rp (precursor coincidence rate) and rt (trigger coincidence rate). Here, rp is defined as the number of event coincidences divided by the number NA of events of type A, describing the fraction of events of type A that have been preceded by at least one event of type B. In turn, rt is defined as the number of event coincidences divided by the number NB of events of type B, thereby describing the fraction of events of type B that have been followed by (and, thus, potentially triggered) at least one event of type A. When using τ 6= 0, this differentiation is essential. A schematic illustration of the two different types of coincidence rates can be found in **Figure 1**.

In addition to the simple calculation of coincidence rates, the R package CoinCalc used in this work for performing the corresponding analyses provides different possibilities to test whether the empirically found coincidence rates are significantly different from what could result from two independent random event sequences (Siegmund et al., 2016). In this work, we will exclusively utilize an analytical significance test based on the assumption of Poissonian event statistics (Donges et al., 2011, 2016; Siegmund et al., 2015).

#### 2.3.2. Conditional and Joint Event Coincidence Analysis

As a thorough extension of the basic ECA method for two event sequences, in this work, we introduce new multivariate generalizations of ECA termed conditional event coincidence analysis (CECA) and joint event coincidence analysis (JECA). While the above formulation of bivariate ECA is sufficient for many applications (e.g., in Donges et al., 2011), in order to analyze the reaction of ecological variables to extraordinary meteorological conditions, it may be important to additionally consider the conditioning of events on specific situations governed by a third observable, i.e., the case that events of type B appear simultaneously with events in series A if and only if also, one or more events of a third type C take place. This conditioning could, for instance, be relevant to account for the interplay between temperature and moisture or between moisture and radiation.

The conditioning of events of type B by events of type C can be described by the precursor coincidence between B and C. Therefore, the conditional precursor coincidence rate rcp and the conditional trigger coincidence rate rct between A and B can be defined (in analogy to rp and rt as mathematically defined by Equations (3) and (4) in Donges et al., 2016) as:

$$\begin{aligned} r\_{cp}(\Delta T, \mathfrak{r}, \Delta T\_{cond}, \mathfrak{r}\_{cond}) &= \\ \frac{1}{N\_A} \sum\_{i=1}^{N\_A} \Theta \left[ \sum\_{j=1}^{N\_B} \Theta \left[ \sum\_{k=1}^{N\_C} \mathbbm{1}\_{[0, \Delta T\_{cond}]} (\mathfrak{t}\_j^B - \mathfrak{r}\_{cond}) - \mathfrak{t}\_k^C \right] \right] \\ \mathbbm{1}\_{[0, \Delta T]}(\{\mathfrak{t}\_i^A - \mathfrak{r}\} - \mathfrak{t}\_j^B) \end{aligned} \tag{1}$$

and

$$\begin{aligned} r\_{\text{cf}}(\Delta T, \text{r}, \Delta T\_{cond}, \text{r}\_{cond}) &= \\ \frac{1}{N\_{B,cond}} \sum\_{j=1}^{N\_B} \Theta \left[ \sum\_{i=1}^{N\_A} \Theta \left[ \sum\_{k=1}^{N\_C} \mathbbm{1}\_{[0, \Delta T\_{cond}]} (\{t\_j^B - \tau\_{cond}\} - t\_k^C) \right] \right] \\ \mathbbm{1}\_{[0, \Delta T]}(\{t\_i^A - \tau\} - t\_j^B) \Big], &\tag{2} \end{aligned}$$

respectively. Here, {t A i }, {t B j } and {t C k } are the timings of the events of types A, B and C, respectively, NC is the number of events of type C, 1Tcond is an additional tolerance window for the condition, τcond a time lag parameter for the condition, and NB,cond is the number of conditional events of type B, i.e., the number of events of type B that show a precursor coincidence with at least one event of type C. 2(·) denotes the Heaviside function (i.e., takes a value of one whenever the argument is nonnegative, and zero otherwise) and 1I the indicator function of the interval I (i.e., takes a value of one whenever the argument is within I, and zero otherwise), respectively. In order to visualize the basic idea of the corresponding CECA, **Figure 1** illustrates the approach in a conceptional way.

Using the definitions in Equations (1) and (2), the conditional precursor coincidence rate describes the fraction of events in series A, that appear simultaneously with C-conditioned events of type B, and the conditional trigger coincidence rate is the fraction of C-conditioned events of type B that are followed by at least one event in series A. In the special case of a simultaneous occurrence of events of types B and C (i.e., τcond = 0), we obtain a setting referred to as JECA.

#### 2.3.3. Methodological Setting in the Present Study

For the application of ECA and CECA/JECA, we dissect the 1095 days period from 2012 to 2014 by sliding windows. For the (bivariate) ECA, the window length is chosen as 61 days with a step size of 5 days, resulting in 75 windows per growing season (1 April to 30 September), where each window contains six events

that are preceded by at least one event of type C. This conditioning is expressed by a precursor coincidence between events of type B and type C. While 1T and τ denote the tolerance window and time lag parameter for counting coincidences between events of types A and B, 1Tcond and τcond are the respective parameters for the conditioning of events of type B on events of type C.

on average. The window length of 61 days is a compromise between a desired high temporal resolution and a possible large window size necessary to produce robust statistics. The step size of 5 days was selected in order to mimimize the computational demand. For the (multivariate) CECA/JECA, the window length is extended to 91 days including nine events on average, since due to the additional conditioning, the number of events in the meteorological variables decreases markedly. In order to cope with the high computational demand of CECA/JECA for 15 variables, the window step size was increased to 10 days, resulting in 15 windows per season.

As a first step, using the sliding window approach, ECA was performed between each of the 15 individual meteorological variables and each tree's SRV series across each window separately. For every window, the fraction of trees with a significant number of coincidences was taken as a proxy describing the reaction of the species to the considered meteorological events. Subsequently, JECA was performed

between the dendrometer data and all pairs of meteorological variables. As a consequence, the chosen analysis setting results in 15 × 14/2 = 105 different variable combinations. Note that for τcond 6= 0, i.e., mutually shifted occurrences of events in the two considered meteorological variables, the number of combinations to be considered in an actual CECA would be twice as large. Therefore, we do not consider mutually conditioned events in this pilot study, but leave a corresponding detailed investigation as a subject of future work. Besides this, we do not consider the possible extension of multivariate conditions (involving different meteorological variables), which would be straightforward yet lead to an even larger combinatorial variety of different cases to be studied.

In all analyses discussed in the remainder of this work only the (conditional) precursor coincidence rates are considered unless stated otherwise.

#### 2.3.4. Cluster Analysis

In order to analyze the simultaneity of event timings between the individual trees, we additionally use the well established approach of hierarchical cluster analysis with complete linkage. Core of the concept of cluster analysis (in this case for dendrometer time series) is a similarity measure, calculated between all possible combinations of individual time series. This similarity measure is, classically, a correlation coefficient. In this study, we additionally introduce the application of event coincidence rate as similarity measure for the cluster analysis. The calculation of the event coincidence rate between each pair of dendrometer event time series follows along the above mentioned approach, using τ = 1T = 0. Due to the above described data preprocessing, there is no difference between precursor and trigger event coincidence rate.

#### 3. RESULTS

#### 3.1. Event Coincidence Analysis with Individual Meteorological Variables

**Figure 2** shows the fraction of trees with significant precursor coincidence rates between extraordinary positive/negative SRVs and positive/negative events in each of the 15 meteorological variables at the same day (1T = τ = 0).

For positive SRV events (**Figure 2**, left panel), five main observations are made: (i) Tmin and Tmax events have an opposite effect in almost all years and for all tree species. Extraordinary positive SRV events mainly coincide with Tmin values above the 90th percentile and Tmax values below the 10th percentile. The same observation is also clearly visible for the land surface temperature. (ii) Extraordinary soil temperature generally has a much lower impact on positive SRV events than extraordinary air temperature. Except for 2013 for beech, STmean and STmax only rarely show coincidences with positive or negative SRV events, while at least extraordinary positive minimum soil temperatures often coincide with positive SRV events. (iii) Extraordinary high values of relative humidity coincide with positive SRV events in all years and for all species. The fraction of trees showing significant coincidences has been slightly reduced in 2014 in comparison with the other 2 years. (iv) Extraordinary high precipitation values do almost continuously coincide with positive SRV events. An important exception is the summer of 2013, where neither tree species showed a corresponding significant relationship. (v) Extraordinary low values of mean and maximum radiation, generally show pronounced coincidences with SRVs above the 90th percentile.

In comparison to positive SRV events, negative events clearly show fewer significant coincidences with extraordinary meteorological conditions (**Figure 2**, right panel). However, two features can be highlighted: (i) For beech, values above the 90th percentile of the maximum temperature strongly coincide with strong negative dendrometer anomalies in 2012, less distinct in 2013, and hardly ever in 2014. In turn, negative oak and pine SRV events do not coincide significantly with maximum temperature events. (ii) Extraordinary low values of relative humidity very often coincide with negative SRV events for all species. This feature is variably expressed during the 3 years of observations but particularly evident in 2012. In contrast, during 2014 maximum relative humidity events (very wet days) significantly coincide with negative beech SRV events.

In addition to the consideration of exactly simultaneous coincidences as described above, **Figure 3** shows the results of precursor ECA using a tolerance window spanning the previous 2 days (1T = τ = 1 day), i.e., this kind of analysis takes into account responses with a time lag of 1 and 2 days. While in this case, positive dendrometer anomalies only show few coincidences with extraordinary meteorological conditions, on the contrary extraordinary negative stem size changes exhibit three major patterns: (i) Maximum land surface and maximum air temperature events show coincidences of their highest values with negative SRV events, which is especially distinct for beech in 2013. (ii) Extraordinary minimum and mean relative humidity values above the 90th percentile show clear coincidences with negative SRV events in 2012 and 2013 for all tree species. (iii) Extraordinary low mean and maximum radiation values coincide with negative SRV events as well. The two last features are mainly visible for beech and pine and are most distinct in 2012 and 2013. Notably, these results are similar to the previous analysis for positive SRV events when using 1T = τ = 0 (see **Figure 2**).

#### 3.2. Joint Event Coincidence Analysis for Paired Meteorological Variables

Due to the large number of possible combinations between meteorological variables, the figures showing the results of JECA are provided in the supplementary material. The following analysis concerns equally directed events if the values of both meteorological variables under study either both exceed their respective upper threshold value or both fall below their respective lower threshold value used for the definition of events. In the other case where one variable takes extraordinarily high values and the other extraordinarily low ones or vice versa, we will speak of oppositely directed events.

#### 3.2.1. Beech

Figure S1 shows the results of JECA between beech SRV events and equally as well as oppositely directed events of each pair

of meteorological variables, respectively. Six main observations are to be highlighted (which are also evident for the two other tree species but to different degrees): (i) In 2012, extraordinary high minimum temperatures in combination with extraordinary high relative humidity strongly coincide with positive SRV events. (ii) Various combinations of all temperature variables coincide with positive SRV events, but for beech almost only in 2014. (iii) Extraordinary low maximum land surface as well as low maximum air temperature events in combination with extraordinary low mean and maximum radiation coincide with positive SRV events, mainly in 2012 and 2013. (iv) Extreme precipitation plays a rather minor role for beech SRV events when applying JECA for equally directed meteorological extremes. In turn, the combinations of extraordinary low maximum air or land surface temperature with extraordinary high precipitation or extraordinary high air humidity strongly coincide with positive beech SRV events. In addition, (v) extraordinary high minimum temperature values together with extraordinary low radiation, and (vi) extraordinary humid conditions (in terms of strong precipitation or high air humidity) again together with

extraordinary low radiation also coincide with positive beech SRV events.

The investigation of negative beech SRV events using JECA shows hardly any significant coincidences (see Figure S2). Whenever evident, the behavior is simply opposite to the effects of positive change anomalies and shall therefore not be further detailed here.

#### 3.2.2. Oak

The results of JECA for oak SRV events are provided in Figure S3. The left panel shows again that (i) during a period in early summer 2012 extraordinary high minimum temperatures in combination with extraordinary high air humidity coincide with positive SRV events, and (ii) various combinations of extraordinary high temperatures coincide with positive oak SRV events as well. Unlike beech, oak stem variations also show this feature in 2013. (iii) Additionally, extraordinary low maximum air and land surface temperatures together with extraordinary low radiation coincide with positive SRV events. (iv) Extraordinary high minimum and mean temperatures together with extraordinary precipitation events appear simultaneously with positive SRV events mainly in 2014 and partly in 2013. In contrast to this, similar to beech SRV events, negative maximum temperatures events with co-occuring extraordinary humid conditions coincide with positive oak SRV events in all 3 years. The features (v) and (vi) are very similar to what has been illustrated in Figure S1 for beech.

For joint coincidences between negative oak SRV events and pairs of meteorological variables (Figure S4), the results are hardly significant in general. However, three specific observations can be made regarding conditions in late summer 2013: (i) Negative SRV events coincide with very dry conditions indicated by the various combinations of extraordinary low humidity as well as (ii) positive maximum temperature events together with negative humidity events and (iii) negative humidity with positive mean and/or maximum radiation events.

#### 3.2.3. Pine

The features (i), (ii), and (iii) indicated in Figure S5 are very similar to the corresponding features in Figure S1. Yet, feature (i) for positive pine SRV events is additionally visible in 2013. The positive impact of very humid conditions (iv) in terms of combinations of extraordinary high air humidity and extraordinary strong precipitation as well as of low maximum air and land surface temperatures and strong precipitation or high air humidity is clearly visible in all 3 years of observations. The features (v) and (vi) reported above are also clearly visible and in general more distinct than for beech and oak.

Negative pine SRV events (Figure S6) coincide with the same extraordinary meteorological conditions that were observed for oak.

#### 3.3. Positive SRVs and Time Lagged Negative SRVs

As already mentioned in Section 3.1, when comparing the results of ECA without time lag and tolerance window with the results using 1T = τ = 1 day, one very important feature is that relative humidity and radiation show coincidences for both, positive (1T = τ = 0) and negative (1T = τ = 1 day) SRV events. A very similar finding was also reported by van der Maaten et al. (2013), based on correlation analysis. The interpretation of this observation leads to the hypothesis that in many cases, after a positive SRV event a negative event occurs during one of the two following days. In order to further test this hypothesis, we also performed ECA between negative SRV events and positive SRV events for previous days. For this purpose, we used negative events (see Section 2) as event series A and positive events as series B with 1T = 2 days and τ = 1 day. In this analysis a precursor coincidence is found, if a negative event is preceded by a positive event at one of the three previous days, whereas a trigger coincidence is observed if a positive event is followed by a negative event during one of the following 3 days. This analysis was performed for all tree species and for each year separately. **Table 1** summarizes the results which indicate very high rates of both trigger and precursor coincidence. Notably, a very high fraction of positive SRV events (up to 64%) precede negative events at one of the three consecutive days. In all these cases, the observed positive SRV events very likely do not correspond to irreversible growth, but rather reversible swelling. On the other hand, the remaining positive events (40–50%) have not been followed by negative events, i.e., either not followed by stem radius decrease at all or by a gradual decrease that is not identified as extraordinary with the employed event definition.

To further investigate this question, we additionally used JECA with the same setup, where the positive SRV events (series B) have been observed in parallel with extraordinarily high rH values (series C). **Table 2** summarizes the results of this analysis. Notably, the observed joint trigger coincidence rates are clearly higher than the trigger coincidence rates in **Table 1** which implies that positive SRV events induced by high air humidity are more likely to be followed by negative SRV events than other positive SRV events. A possible scenario consistent with this finding would be thunderstorms during relatively dry and/or hot periods, where wet conditions induce positive SRVs and rapid hydrological processes return to dry soil conditions very quickly again. Moreover, we find that the joint trigger coincidence rates are distinctively higher than the joint precursor coincidence rates. This indicates that most of the humidity-induced positive SRV events have triggered negative events, but only a smaller fraction of negative events have been preceded by humidityinduced positive SRV events. This finding suggests, that there are different types of positive SRV events (followed or not followed by negative SRV events events) and different types of negative SRV events events (preceded or not preceded by positive SRV events).

### 3.4. Coincidences of the Timings of SRV Events between the Individual Trees

When comparing the results of both bi- and multivariate ECA between the three tree species, the differences are relatively small. Altogether, oak seems to not favor wet conditions as strongly as beech and pine, but systematic inter-species differences appear to be absent. In turn, for the mean behavior (of daily as well as

TABLE 1 | Mean precursor and trigger coincidence rates (10 trees per species) between negative (event type A) and positive SRV events (event type B), using 1T = 2 days and τ = 1 day (i.e., a negative event following a positive events).

TABLE 2 | Mean joint precursor and joint trigger coincidence rates (10 trees per species) between negative SRV events (series A), positive SRV events (series B) and extraordinarily high rHmean events (series C), using 1T = 2 days, τ = 1 day, 1Tcond = 0 and τcond = 0.

subdaily features), the growth characteristics are widely known to differ markedly between different tree species (Drew and Downes, 2009; Miralles-Crespo et al., 2010; Köecher et al., 2012; Butt et al., 2014). The question, whether there are differences or commonalities regarding the tree species' upper or lower parts of the distribution of SRVs has not been addressed to far. In order to investigate this issue for the three species of this study, we performed a hierarchical cluster analysis as described in Section 3. **Figure 4** shows the results of this analysis and reveals that although the mutual correlations between the individual trees are quite high, the coincidence rates between days with positive SRV events are comparatively low. Based on correlation, we additionally find that the tree individuals are quite well clustered, while when using coincidence rates as similarity measure, the clusters following the individual species are completely lost. This means that the highest SRV variations of the individual trees vary strongly in their timing (at the daily scale), and that this timing is not generally differing by tree species. Hence, a clear systematic difference of the results between the different species (Section 3) cannot be found.

## 4. DISCUSSION

#### 4.1. Bivariate Event Coincidence Analysis

The two broadleaved species show a positive response to temperature as well as LST while little positive response is found for pine. This positive growth response is likely due to the sufficient supply of water; possibly by reaching groundwater reservoirs. Similar relationships have been found earlier for the mean values (i.e., using correlation-based analyses) by van der Maaten et al. (2013) and others. In contrast to this, the fact that extraordinary negative Tmax and/or LSTmax events also coincide with positive SRV events (for all species) delivers important complementary information to the positive correlation between stem size variations and Tmax found by van der Maaten et al. (2013) and Deslauriers et al. (2003). Due to the fundamentally different nature of these two statistical approaches, two time series can be positively correlated and simultaneously show significant coincidences between negative and positive events. This is not contradictory, but complementary information. The latter finding is further strengthened by the strong coincidence between positive Tmax events and negative beech SRV events. Since both van der Maaten et al. (2013) and Deslauriers et al. (2003) performed their analysis for the entire (not subdivided) growing season, it is not possible to compare the seasonal timings of these contradicting behaviors.

Only few coincidences were found between SRV events and soil temperature extremes. It is likely that this observation is due to the location of the meteorological station 2 km from the dendrometer site. Due to the variability in soil type and ground cover throughout in the study area, actual soil temperatures beneath the sampled trees may systematically differ from the values measured at the station.

A positive instantaneous (lag zero) correlation between air humidity and SRV has been observed in previous studies (Downes et al., 1999; Deslauriers et al., 2003; Köecher et al., 2012; van der Maaten et al., 2013). In the present work, it was confirmed that this relationship is also evident for the upper (positive events) and lower (negative events) tails of the distribution of SRVs. As may be expected, a similar positive SRV coincidence is recorded with precipitation. However, the absence of a notable heavy precipitation impact during summer 2013 in all tree species is a result of a 60-days dry period except for one single day (35 mm). This suggests that one single heavy rainfall event is not sufficient to result in a significant coincidence rate even if it coincides with a marked positive or negative dendrometer anomaly (which was the case for almost all 30 trees).

The significant coincidences between low radiation values and positive SRV events can interpreted in a twofold way: One the one hand, low radiation decreases transpiration leading to water replenishment. On the other hand, low radiation days commonly correspond to cloudy and foggy conditions and are therefore often characterized by high relative humidity as well. A general negative dependency in terms of negative correlations between radiation and stem radius variability was reported earlier by Downes et al. (1999) and Köecher et al. (2012).

Our analysis revealed some counter-intuitive significant coincidences between days with extraordinary high air humidity and negative SRV events in beech stems during 2014 (**Figure 2**). One possible cause for this may be the result of high air associated with low soil moisture conditions typical of foggy days during spring. Further support of this theory is provided by Figure S2, where in the upper right panel joint coincidences between low Tmin, high rHmax and negative beech SRV events are evident. In order to understand these joint coincidences in more detail, **Figure 5** illustrates the temperature and air humidity development of 4 days in spring and early summer 2014, where the above mentioned coincidences appeared. Specifically, the figure shows very high air humidity values of up to 99% around 9–10 a.m. which abruptly decrease simultaneously with increasing temperature. This behavior is a common indication of foggy conditions during morning hours—caused by inverted atmospheric stratification—that are relieved by the rapidly increasing temperatures on a cloud-free day. The negative SRV events of these days are caused by the extraordinary high temperature and low humidity values of the mid-day and are not linked with the high air humidity values of the morning hours. Applying a classical linear correlation analysis between daily maximum relative humidity and daily SRVs, these days would produce strong residuals, clearly deviating from the well-known positive statistical interrelationship between these two variables. Therefore, these specific days provide a very good example of how to explain singular large residuals in classically assumed interrelationships between weather conditions and dendrometer variations as concluded from correlation analysis on a daily basis.

The results of bivariate (**Figures 2**, **3**), as well as multivariate (Figures S1–S6) event coincidence analysis show that for a number of variables, coincidences with SRV events are not equally evident during all the 3 years of the investigation period. One example was mentioned in the previous paragraph. Another example is that positive Tmax events only coincide with negative beech SRV events in 2012 and 2013, but not in 2014 (**Figure 2**, upper right panel). In this case, the reason is, that the positive Tmax events in 2014 are, in absolute values, lower than the

positive Tmax events of the previous 2 years. Even though the events were defined by percentiles over the entire investigation period, a high number of positive Tmax events in 2012 and 2013 led to this non-uniform distribution of events. In other words: in 2014, the highest Tmax events were not warm enough to trigger negative SRV events. The same explanation is also valid for the absence of coincidences between positive SRV events (for all species) with LSTmin, STmin and STmean in 2012. While in these cases the heterogeneous distribution of coincidences among the years can easily be explained, in other cases the reasons are less obvious. One possible factor leading to interyear differences in the relation between weather conditions and SRVs could be the effect of the previous year's weather conditions on the current tree growth. Corresponding impacts on earlywood production (especially for oak, but also for the other two species) have been reported by a variety of studies (Lebourgeois, 2000; Rubino and McCarthy, 2000; Lebourgeois et al., 2004, 2005; Drobyshev et al., 2008; Michelot et al., 2012; Latte et al., 2015). Yet, all previous analyses that the authors are aware of, addressed integrated/cumulative stem size rates in terms of total earlywood production. So far, no statistical evidence or physiological explanation has been reported, why and how previous year's conditions should influence the day-to-day SRVs and their reaction to weather extremes.

## 4.2. Extraordinary Positive vs. Negative Stem Size Variation Events

The counter-intuitive negative beech SRV events found in Figure 2 and discussed in Section 4.1 could also be explained by a statistical artifact due to the co-occurrence between negative SRV events and positive SRV events of the previous day. Such a phenomenon of contradicting climatic signals in tree rings has also been reported for high resolution tree-ring isotope data (Schollän et al., 2013, 2014).

The various cycles of swelling and shrinkage shown by dendrometer data have been addressed in several recent studies (Downes et al., 1999; Köecher et al., 2012; van der Maaten et al., 2013; Vieira et al., 2013). From previous studies like Bouriaud et al. (2005), it is known that stems can shrink over several consecutive days, likely induced by shrinkage of the bark and

relative sap-flow reduction of the stem some time after rain events. Our study refined these findings by pointing out that there are two kinds of strongly positive stem size variations: (1) some that are followed by negative SRV events and (2) some that are not followed by negative stem size changes during consecutive days. For future studies it will be important to investigate how to disentangle the four possible combinations of strong stem size changes defined in this study: There are strong positive SRV events that are followed (i.e., neutralized) by negative SRV events vs. positive SRV events that permanently increase the stem radius, as well as negative SRV events that simply originate from strong positive SRV events during the previous days vs. negative SRV events that have been forced by adverse weather conditions. A first attempt to disentangle these distinct phenomena has been recently published by Chan et al. (2016). The mentioned classical approach do define growth and shrinkage in dendrometer data (Deslauriers et al., 2003; Bouriaud et al., 2005; Köecher et al., 2012; Vieira et al., 2013) does not solve this problem, since (as for the procedures used by van der Maaten et al. (2013) and also in this study) both shrinkage and growth are solely defined based upon the preceding evolution and do not take into account the (short or long-term) following development of the stem radius.

#### 4.3. Joint Event Coincidence Analysis

The JECA revealed six main findings common to all three investigated tree species. (i) The combination of high minimum temperature with high relative humidity events coinciding with positive SRV events describes situations of warm nights followed by moist days. This feature was most clearly visible for pine which is to be explained by pine having the highest potential for water storage due to its larger amount of xylem Pfautsch et al. (2015). (ii) The positive impact of various temperature combinations on stem radius is a logical continuation of the results of bivariate ECA as discussed above. (iii) The combination between low radiation and low maximum temperatures describes very cloudy days. Such days are often also characterized by high air humidity, and this combination (high humidity and low radiation) is highlighted by finding (vi). (iv) Heavy precipitation as an additional event contributing to the aforementioned cloudy and moderately cool days also favors strongly positive SRVs. Such days are often also characterized by high night temperatures (high Tmin) corresponding to situation (v) highlighted in Section 3.2.

## 4.4. Differences in the Timing of Stem Size Variation Events between the Species

The observations found in Section 3.4 are in fact not trivial, since very different reactions of the analyzed species to environmental conditions have been well documented by, e.g., Gonzalez-Munoz et al. (2014), Lévesque et al. (2013), Garcia-Suarez et al. (2009), or Kwiaton and Wand (2015). The difference of our analysis to these studies is, that our dendrometer analysis specifically takes into account the timings of SRVs and meteorological extremes on a daily basis, whereas previous studies analyzed relationships between tree stem growth and weather conditions on a seasonal time scale. Therefore, the finding that no clear species-to-species differences are evident in this study does not contradict studies on seasonal scales. In turn, our results suggest, that the species-specific relations on weather conditions are more clearly expressed on longer rather than on shorter time scales. Nonetheless, our study indicates, that the different species do not differ markedly in their susceptibility to climate extremes on a daily scale. General statements or even suggestions to forest management concerning the species' eligibility in the context of ongoing and future climate change should not yet be drawn from this first case study.

## 5. CONCLUSIONS

We have used high-resolution dendrometer data to investigate tree species-specific responses to extraordinary meteorological conditions. For the first time joint event coincidence analysis as well as a hierarchical clustering analysis based on coincidence rates have been used. This new approach allowed a detailed analysis of the timing of observations falling in the upper and lower tails of the empirical distributions of daily SRVs. This opens new possibilities for interpreting tree-specific responses to meteorological extremes. Our method is able to provide relevant complementary information beyond what has been known from previous correlation-based analyses. Further potential applications of this method include the investigation of dendrochronological data or intra-annual density fluctuations (IADF).

For future investigations, it will be crucial to put additional efforts into disentangling tree stem radius growth from stem swelling, using novel data analysis approaches. Additionally, integrated studies including dendrometer and wood density measurements, as well as an up-scaling across a larger area will be necessary to draw reliable conclusions on tree or forest carbon storage dynamics in relation to meteorological extreme events.

#### AUTHOR CONTRIBUTIONS

JS Study's Design, Data Analysis, Figures, Writing. TS Study's Design, Proofreading. IH Data Production and Preprocessing, Proofreading. EV Proofreading. SS Data Production and Preprocessing. GH Study's Design, Proofreading. RD Study's Design, Writing, Proofreading.

#### ACKNOWLEDGMENTS

This study was conducted within the framework of the Young Investigators Group "CoSy-CC2 : Complex Systems Approaches to Understanding Causes and Consequences of Past, Present and Future Climate Change" (grant no. 01LN1306A) funded by the German Federal Ministry for Education and Research (BMBF), the COST Action FP1106 STReESS supported by COST (European Cooperation in Science and Technology), the Virtual Institute of Integrated Climate and Landscape Evolution Analysis—ICLEA—(grant no.

#### REFERENCES

VH-VI-415), and the Terrestrial Environmental Observatories project—TERENO—of the Helmholtz Association. Jonatan Siegmund acknowledges financial support by the Evangelisches Studienwerk Villigst e.V. Ingo Heinrich received support from the Deutsche Forschungs-Gemeinschaft (DFG project number He 7220/1-1). The authors wish to thank Jonathan Bauermann and Jonathan Donges for fruitful discussions on the notation of CECA. Thanks to Daniel Balanzategui for proof-reading the manuscript. All presented analyses have been performed using the R package CoinCalc, available at https:// github.com/JonatanSiegmund/CoinCalc. The publication of this article was funded by the Open Access fund of the Leibniz Association.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016. 00733

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Siegmund, Sanders, Heinrich, van der Maaten, Simard, Helle and Donner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Structure and Function of Intra–Annual Density Fluctuations: Mind the Gaps

Giovanna Battipaglia1, 2, 3 \*, Filipe Campelo4 , Joana Vieira4 , Michael Grabner 5 , Veronica De Micco6 , Cristina Nabais 4 , Paolo Cherubini 7 , Marco Carrer 8 , Achim Bräuning9 , Katarina Cufar ˇ 10, Alfredo Di Filippo11, Ignacio García-González 12, Marcin Koprowski 13 , Marcin Klisz 14, Alexander V. Kirdyanov 15, 16, Nikolay Zafirov 17 and Martin de Luis 18

#### Edited by:

Judy Simon, University of Konstanz, Germany

#### Reviewed by:

J. Renee Brooks, United States Environmental Protection Agency, USA Rupert Wimmer, Universitaet für Bodenkultur Wien, Austria

\*Correspondence:

Giovanna Battipaglia<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

Received: 12 January 2016 Accepted: 18 April 2016 Published: 06 May 2016

#### Citation:

Battipaglia G, Campelo F, Vieira J, Grabner M, De Micco V, Nabais C, Cherubini P, Carrer M, Bräuning A, Cufar K, Di Filippo A, ˇ García-González I, Koprowski M, Klisz M, Kirdyanov AV, Zafirov N and de Luis M (2016) Structure and Function of Intra–Annual Density Fluctuations: Mind the Gaps. Front. Plant Sci. 7:595. doi: 10.3389/fpls.2016.00595 1 Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Second University of Naples, Caserta, Italy, 2 Centre for Bio-Archaeology and Ecology, PALECO Ecole Pratique des Hautes Etudes, Institut des Sciences de l'Evolution, University of Montpellier 2, Montpellier, France, 3 Euro-Mediterranean Center on Climate Change, Lecce, Italy, 4 Department of Life Sciences, Centre for Functional Ecology, University of Coimbra, Coimbra, Portugal, 5 Institute of Wood Technology and Renewable Resources, University of Natural Resources and Life Sciences, Vienna, Austria, 6 Department of Agricultural Sciences, University of Naples Federico II, Naples, Italy, 7 Swiss Federal Research Institute WSL, Birmensdorf, Switzerland, 8 Department of Land, Environment, Agriculture and Forestry, University of Padua, Padua, Italy, 9 Department of Geography and Geosciences, Institute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 10 Department of Wood Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia, 11 Department Agricultural and Forestry, Università degli Studi della Tuscia, Viterbo, Italy, 12 Departamento de Botánica, Escuela Politécnica Superior, University of Santiago de Compostela, Lugo, Spain, 13 Faculty of Biology and Environment Protection, Nicolaus Copernicus University, Toruñ, Poland, 14 Department of Silviculture and Genetics, Forest Research Institute in Poland, Budynek, Poland, 15 V.N. Sukachev Institure of Forest SB RAS, Krasnoyarsk, Russia, 16 Department of Math Methods and IT, Siberian Federal University, Krasnoyarsk, Russia, 17 Department of Plant Pathology and Chemistry, University of Forestry, Sofia, Bulgaria, 18 Department of Geography and Regional Planning—IUCA, University of Zaragoza, Zaragoza, Spain

Tree rings are natural archives of climate and environmental information with a yearly resolution. Indeed, wood anatomical, chemical, and other properties of tree rings are a synthesis of several intrinsic and external factors, and their interaction during tree growth. In particular, Intra-Annual Density Fluctuations (IADFs) can be considered as tree-ring anomalies that can be used to better understand tree growth and to reconstruct past climate conditions with intra-annual resolution. However, the ecophysiological processes behind IADF formation, as well as their functional impact, remain unclear. Are IADFs resulting from a prompt adjustment to fluctuations in environmental conditions to avoid stressful conditions and/or to take advantage from favorable conditions? In this paper we discuss: (1) the influence of climatic factors on the formation of IADFs; (2) the occurrence of IADFs in different species and environments; (3) the potential of new approaches to study IADFs and identify their triggering factors. Our final aim is to underscore the advantages offered by network analyses of data and the importance of high-resolution measurements to gain insight into IADFs formation processes and their relations with climatic conditions, including extreme weather events.

Keywords: IADF, tree-ring, wood anatomy, stable isotopes, network analysis, wood formation

### IADF FORMATION AND POSITION

Intra-Annual Density Fluctuations (IADFs) are variations in wood density that are defined by the presence of earlywood-like cells within latewood and (or) by the presence of latewood-like cells within earlywood (Fritts, 2001). Such anatomical structures may hamper cross-dating and any further analyses of treering series (Cherubini et al., 2003). Thus, IADFs have long been considered by dendrochronologists as the "Ugly Duckling" of wood anatomical features, and species forming them have often been discarded for climate reconstructions (Lorimer et al., 1999) and used as indicators of particular events such as flood-regime or air pollution (see Wimmer, 2002 for review). During the 2000s, the "Ugly Duckling" turned into a "Beautiful Swan," when different studies demonstrated the potential of these anatomical features for ecological, environmental, and climatological interpretations (Wimmer et al., 2000; Rigling et al., 2001; De Micco et al., 2007; Campelo et al., 2007a,b; De Luis et al., 2007). Since then, the importance of IADFs has been widely recognized and the number of papers dealing with them has increased significantly (De Micco et al., 2016). It has been underlined that IADFs may provide accurate information at the seasonal level (Glock and Agerter, 1960; Tessier et al., 1997; Bräuning, 1999; Campelo et al., 2007b; Battipaglia et al., 2010, 2014; De Micco et al., 2012), allowing a more detailed climate analysis within the growing season (Wimmer et al., 2000; Novak et al., 2013a,b, 2016).

Variations in IADF features can be used to reconstruct past environmental conditions, and IADF relative position within the ring can be used to estimate when a specific environmental factor occurred (Figure S1). Campelo et al. (2007b) classified a band of latewood-like cells at the end of earlywood in Pinus pinea as an "IADF type E+," corresponding to a gradual transition from early- to latewood (Figure S1). They suggested that those IADFs could be linked to soil water conditions during late spring, hypothesizing that rainfall events in late spring could delay the transition from early- to latewood. Later in the growing season the cambium can reverse latewood production forming again earlywood-like cells. When a band of latewood-like cells is located within earlywood the IADF is labeled as "type E" (Campelo et al., 2007b). This type of IADF (Figure S1) seems to be uncommon in Mediterranean pine species (Vieira et al., 2010; Rozas et al., 2011; Campelo et al., 2013) probably because regular weather conditions during spring can assure continuous growth (Olano et al., 2012), or trees are able to minimize episodic events of water stress during the early growing season (Loustau et al., 1996; Borghetti et al., 1998). In contrast, the formation of IADF type E was found to be rather frequent in other environments: for example, in Pinus nigra sampled in the Vienna Basin, this type of IADF was triggered by a combination of wet April, dry May, and wet June and it was related to the watertable level (Wimmer et al., 2000). In Erica arborea and Arbutus unedo, hardwood species growing in the Mediterranean basin, this type of IADF was also frequent and triggered by summer drought conditions (Battipaglia et al., 2010, 2014; De Micco et al., under revision). In hardwood species the comparison of series of vessel lumen size between tree rings with and without IADFs suggested that: (a) IADF position is related to the period of the season when stressful conditions priming the fluctuation occur, (b) the width of the IADF indicates the duration of conditions triggering its formation (Campelo et al., 2007a; De Micco et al., 2014). Most studies dealing with IADFs found that their frequency increased close to the end of the tree ring (Rigling et al., 2001; Rozas et al., 2011). Two types of latewood IADFs have been classified considering the position within latewood: the first type is characterized by earlywood-like cells within latewood (IADF L; Figure S1) and the other located between latewood and earlywood of the next ring and characterized by intermediate anatomical traits (IADF L+; Figure S1) (Campelo et al., 2007b). In both cases, they were mainly associated with favorable conditions occurring after the summer drought, in early autumn (L) or in late autumn (L+) (Rigling et al., 2001; Masiokas and Villalba, 2004; Campelo et al., 2007b; Battipaglia et al., 2010, 2014). Although, this first classification could be criticized for the fact that IADFs E+ and L+ do not correspond to a true fluctuation in wood density, it is important to question the value of the position of IADFs as a proxy for past climate. By using the relative position of IADFs within tree rings it is possible to improve the temporal resolution of tree-ring series (De Micco et al., 2014), especially in areas where the growing season is long, such as in the Mediterranean region (Rozas et al., 2011; De Luis et al., 2011a).

The identification of the environmental conditions triggering IADF formation is based on linear correlations between climatic variables and IADF chronologies, highlighting the importance of water conditions during the growing season in their formation. However, these correlations are not enough to fully understand the process behind IADF formation, namely at the level of cambial activity and cell differentiation processes. Are IADFs the result of cambial reactivation? Are IADF cells already present in the cambial zone undergoing differentiation? Are latewood IADFs caused by changes in the cell enlargement and/or cell wall deposition phase? These are fundamental questions that can only be answered by monitoring xylogenesis at a weekly time scale, and relating it to intra-ring variations of cell features. Studies on cambial dynamics and wood production can help us to understand the physiological mechanisms behind IADFs formation (Camarero et al., 2010; Vieira et al., 2015). There have been recently major developments in this field, leading to a detailed description of the timings of cambial activity, duration of cell production and differentiation phases and response of cambium to environmental conditions in different species and environments (De Luis et al., 2007; Camarero et al., 2010; Cuny et al., 2014). These studies showed that cambial activity in the Mediterranean region presents a high year-to-year variability, strongly dependent on climate (Vieira et al., 2014a). Cambial activity in the Mediterranean, as in other temperate environments, starts in spring in response to warm temperatures and increasing photoperiod (Vieira et al., 2014b), with periclinal cell divisions of the vascular cambium and the production of earlywood tracheids. It reaches a maximum around May and then, when water becomes less abundant, cambial activity slowly decreases, reaching a minimum in the summer months (Camarero et al., 2010), when latewood tracheids are produced (Uggla et al., 2001). Water availability is fundamental for cell division and turgor-driven cell expansion (Kutschera and Niklas, 2013). Expansion only starts once a threshold of turgor pressure is achieved and the pressure applied by the waterfilled vacuole against the cell wall determines the tracheid final size (Oribe et al., 2003). The formation of the latewood cells is expected during summer, while the earlywood-like cells can be formed in autumn, if favorable climatic conditions return. Several studies under Mediterranean climatic conditions suggested that cambial activity could show a bimodal pattern with two main peaks: one in spring and the other in autumn (De Luis et al., 2011a,b; Battipaglia et al., 2014; Vieira et al., 2015). Since cambial reactivation after a dry summer is not always observed, a facultative bimodal pattern is the best way to describe cambial activity in Mediterranean environments. The tracheids differentiated after summer drought differ from those previously formed in latewood, since their cell wall thickness to lumen diameter ratio is lower than in true latewood (Carvalho et al., 2015; Vieira et al., 2015). Thus, tracheids forming IADFs L have larger radial cell and lumen dimensions than true latewood tracheids. Differentiating tracheids can expand beyond the usual radial diameter of latewood tracheids, if water is available. Indeed, the lumen area of a tracheid depends on turgor pressure and duration of cell enlargement (Cuny et al., 2014). These results suggest that the formation of latewood IADFs in the Mediterranean area are defined during the enlargement phase, whereas it is possible that latewood IADFs formed at higher altitudes and latitudes are caused by changes in the cell wall deposition phase. In colder environments, tracheid differentiation must be concluded before the onset of winter (Rossi et al., 2008) and IADF L+ can be formed if there is not enough time to complete the deposition phase due to a fast drop in air temperatures. However, formation and ontogenesis of this kind of IADFs are still under debate.

#### IMPACT OF IADFs ON TREE HYDRAULICS

One important gap in IADF research is the functional role played by these anatomical structures on tree hydraulics (Wilkinson et al., 2015). It is known that the size of conduits (e.g., tracheids and vessels) is related to the hydraulic conductivity, while protection from drought-induced embolism is a function of the ability to prevent air-seeding and this is strongly related with the number and size of pits, thus indirectly with lumen size (Hacke et al., 2004; Pittermann et al., 2006). Earlywood IADFs, characterized by latewood-like cells within earlywood, potentially represent a fraction of the earlywood with a lower hydraulic conductivity, while the opposite occurs for IADFs located in latewood. Small increases in tracheid lumen can dramatically increase hydraulic conductivity because flow rate is proportional to the fourth power of the tracheid radius (Tyree and Ewers, 1991). Thus, it is important to quantify their impact on tree hydraulics, because currently we only have indirect observations (Campelo et al., 2007b). It can be assumed that all cells forming IADFs are conductive in order to quantify IADFs impact on the total hydraulic conductivity. Afterwards, a more experimental approach is needed to check if IADFs are functional from a hydraulic point of view. It is also important to characterize the cells forming the IADFs, namely their lumen diameter, length, number, and size of pits, as these anatomical characteristics will affect their hydraulic conductivity.

## OBJECTIVE ASSESSMENT OF IADFs

Visual identification of IADFs in conifers is only possible through the analysis of variations in tracheid features (e.g., cell and lumen diameter, and cell-wall thickness). The accuracy of the visual macroscopic identification of IADFs depends on many parameters, such as the quality of wood surface polishing, microscope magnification and criteria used to distinguish IADFs. Since visual identification of IADFs is based on qualitative criteria rather than on quantitative measurements, the subjectivity of the operator can also be one of the major sources of error. Intra-ring variations in tracheid anatomy, and consequently IADFs, can also be identified through quantitative measurements of tracheid features or tracheidograms (Hetzer et al., 2014; Ziaco et al., 2014; Carvalho et al., 2015; Campelo et al., 2016) and image analysis of X-ray densitometry profiles (Cherubini et al., 2013; Gonzalez-Benecke et al., 2015; Wilkinson et al., 2015). Image analysis avoids the long and tedious procedure of visual examination of wood samples and IADF characteristics can be computed automatically (e.g., relative position within the tree ring and IADF-band width). Image analysis also precludes the operator's subjectivity and provides the size distribution of tracheid features (e.g., lumen diameter and cell-wall thickness). However, it is highly recommended that IADFs recognized automatically by algorithms to be compared with those obtained visually by an expert of wood anatomy for an initial calibration to guarantee the correctness of the criteria used for their identification. All general constraints listed for the identification of IADF in softwoods apply also to hardwoods, where IADF analysis is even more complicated due to the occurrence of different cell types and the spatial distribution of vessels, which are usually not arranged in regular rows like softwood tracheids. Further studies are needed because a number of different anatomical functional traits seem to work for IADF identification in hardwoods, but they appear to be species-specific (De Micco et al., 2015). Furthermore, the fact that many features can be involved (e.g., vessel size, fiber wall thickness, spatial arrangement of cells) opens the possibility to define new types of IADFs.

Within this framework, sharpening the focus at the tissue scale and analyzing various xylem histological traits seemingly represents one of the most promising approaches. Recent methodological advances in quantitative wood anatomy (Gärtner and Schweingruber, 2013; Von Arx and Carrer, 2014) allow efficient development of multi-centennial time series of xylem anatomical traits mostly related to the type and number of cells per ring and to cell-lumen and cell-wall dimensions. These advances not only improve the length of the generated timeseries, but most of all the robustness of the measurements. With the currently available computer capacity combined with specific software, accurately tailored to analyze wood anatomical traits, Battipaglia et al. Intra-Annual Density Fluctuation

it is possible to apply a more thorough and unbiased approach considering all cells within each wood anatomical image. This can outperform the previous practice of measuring xylem traits along a few selected radial cell files as it allows information to be collected on hundreds to several thousands of cells per ring (Figure S2). This, together with improved perception of the long-term ontogenetic change in xylem-cell dimension (Carrer et al., 2015), clearly opens the door for a sound statistical analysis not just of IADFs frequency but also on their extension, intensity, position within the ring, or on the relative role of different cell traits to classify different IADFs types that cannot be unambiguously distinguished by visual inspection.

## STABLE ISOTOPES APPROACH

Quantitative wood anatomy has recently been coupled with stable isotope (δ 13C and δ 18O) measurements (De Micco et al., 2007; Vaganov et al., 2009; Battipaglia et al., 2010, 2014) to characterize IADFs, offering new perspectives in the interpretation of IADFs in relation to physiological and ecological processes. What is still unsolved is if the stable isotope signals can help us to identify the different types of IADFs within a ring. Battipaglia et al. (2010, 2014) demonstrated with in continuum stable isotope measurements in hardwood species that IADFs have a unique isotopic signature linked to their position, and are completely different from the correspondent well-known earlywood-latewood isotopic range values (Helle and Schleser, 2004; Vaganov et al., 2009). Here, we performed a preliminary study analyzing IADFs of different species at different sites (P. pinea from Italy, Pinus halepensis from Spain and Slovenia; Pinus pinaster from Portugal, Larix decidua from Poland, and L. decidua x kaempferi from Austria), in order to verify a possible common isotopic signal at intra-annual scale for each type of IADF. IADF L was found in all sites and species, whereas IADF L + and E+ were only present in 66% of the sites and type E only in 50%. The carbon and oxygen isotopic signals of the different kinds of fluctuations were consistent between sites and species with differences between IADFs type E, E+, and L and none between L and L+, supporting the hypothesis that the L+ can be considered as a transitional wood and not as a true fluctuation (Table S1, Figures S3, S4). Although a more complete analysis is required in order to completely understand the link between isotope signals, position, and climatic parameters triggering IADF formation, stable isotopes seem to be a powerful tool not only to increase physiological information on plant responses to climate, but also for the objective identification of each IADF type.

## IADF OCCURRENCE AND NETWORK APPROACH

IADFs have been reported in several species (hardwoods and softwoods), and regions across a wide gradient of temperature and rainfall availability, from tropical to subarctic, to semi-arid and arid environments (De Micco et al., 2016). The majority of studies have been conducted in Mediterranean ecosystems where the highest frequency of IADFs has been reported, particularly in conifers such as Pinus spp., and where several efforts have been made to analyze the characteristics and ecological meaning of IADFs (Bräuning, 1999; Wimmer et al., 2000; Rigling et al., 2002; De Micco et al., 2007; Campelo et al., 2007b, 2013, 2015; De Luis et al., 2007, 2011a,b; Vieira et al., 2009, 2010; Camarero et al., 2010; Rozas et al., 2011; Nabais et al., 2014; Novak et al., 2013a,b). Under boreal or temperate climate, IADFs have been observed in 9% of the tree rings at maximum (Wimmer et al., 2000; Rigling et al., 2001; Copenheaver et al., 2006), while under Mediterranean climate, they have been observed in up to 15–32% of rings (Campelo et al., 2007b; Bogino and Bravo, 2009; Vieira et al., 2009; Novak et al., 2013a).

The role of sex and genetics on the occurrence of IADFs has only recently been investigated. Olano et al. (2015), studying IADF frequency in Juniperus thurifera growing in two sites with contrasting hydrological conditions in Spain, reported that female trees present the highest frequency of IADFs reflecting their opportunistic water use strategy. Treebreeding studies have shown the influence of provenance on tree growth and wood properties (Rozenberg et al., 2002; Hannrup et al., 2004; Klisz et al., under revision). For example, Rozenberg et al. (2002) found that different wood density parameters, including density fluctuations in earlywood and their position within the tree ring showed high heritability. However, the effect of provenances on IADF formation has not been investigated in detail yet, but we can expect that IADF frequency should differ in provenances with different growth rates. As experimental approach, we investigated the influence of provenance on IADF formation by comparing two contrasting provenances (in terms of tree growth) from a long-term trial (1970–2011) of Abies alba (George et al., 2015) grown in eastern Austria. The mean tree-ring width of the fast-growing provenance (Slovakia, P19; 3.3 mm) was twice as wide than the slow-growing provenance (Italy, P45; 1.5 mm). As expected the highest IADF frequency was observed in the fast-growing provenance (**Figure 1**), highlighting the necessity for further investigation of the genetic influence on IADF occurrence.

The consistency of the climatic signal among different pine species and areas suggested that a large-scale network of IADFs in the Mediterranean region could help to study intra-annual climate variability (Zalloni et al., 2016). In the framework of the FPS COST Action FP1106 STReESS (Studying Tree Responses to Extreme Events: a SynthesiS), a catalog and database of IADF occurrence and anatomical and isotopic features have been developed, consisting of data collected on different species across a large geographical range. This unique and novel catalog includes IADF identification and measurements in 10 countries, 14 species, and 108 tree populations with a total of 2199 trees (3670 cores) and 234,262 tree rings. In this perspective we present a first exploratory analysis on IADFs showing a wide range of variability in IADF frequency (**Figure 2**, Table S2), with sites where IADFs are nearly absent (minimum frequency of 0.9% in high-elevation P. nigra on Corsica) and others where IADFs are present in nearly all tree rings (maximum of 93% in P. pinaster in Galicia, Spain). The network approach offers

the mean tree-ring width; the mean tree-ring width in the fast-growing provenance (3.3 mm) was twice as wide than in the slow-growing provenance (1.5 mm).

important advantages since it overcomes limitations due to treeage and tree-size effects (Vieira et al., 2009; Novak et al., 2013a; Campelo et al., 2015) and to local replication (Zalloni et al., 2016). It also provides a unique possibility to interpret the relationship between IADF frequency and the main climate factors promoting their formation at a regional scale as described by Zalloni et al. (2016) for P. halepensis, P. pinea, and P. pinaster across their distribution range.

## CONCLUSIONS

To maximize the extraction of environmental information from IADFs, more researches on IADF formation and data about IADF frequency are needed. There is also a need to classify IADFs more precisely, and to quantify their wood anatomical features. In this context, a network approach could help to identify not only the main climatic drivers of IADF formation, but also to clarify the functional role of IADFs across different environments and species. The catalog presented here will be further explored and new data will be welcome from different environments and species aiming to create a unique network between scientists working with IADFs. This would help us to answer the large number of open questions and to fill the current gaps on IADFs research.

Further, we believe that one urgent issue still under debate is the identification of IADFs using wood quantitative approaches. Until now, each operator has used his own ability (that depends on experience) to recognize IADFs and to assign them to earlywood or latewood. In many tree species, the correct identification of IADFs is more difficult because the transition between earlywood and latewood is not straightforward and unequivocal. Given the subjective nature of IADF identification, the operator must be well trained and experienced. However, an intrinsic error due to the operator's subjectivity will always remain during the process of IADF identification. To overcome this drawback, machine learning based approaches should be specifically developed to recognize IADFs.

## AUTHOR CONTRIBUTIONS

GB and MD gave a substantial contribution to the conception and design of the study. GB, MD, AB, AK, AD, CN, KC, MKl, MK, MG, NZ, IG contributed to the supply of data for the network. MD was in charge of network analyses. GB performed stable isotopes analyses. GB wrote the first draft of the manuscript. MD, FC, JV, VD, CN, MG, MC, PC contributed to writing specific sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted manuscript.

#### ACKNOWLEDGMENTS

This research is linked to activities conducted within the COST FP1106 "STReESS" network. Collection of

### REFERENCES

datasets used for this work was supported by the projects:

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016. 00595

Fritts, H. C. (2001). Tree Rings and Climate. London: The Blackburn Press.

Gärtner, H., and Schweingruber, F. H. (2013). Microscopic Preparation Techniques for Plant Stem Analysis. Remagen: Verlag Dr. Kessel.

George, J. P., Schueler, S., Karanitsch-Ackerl, S., Mayer, K., Klumpp, R. T., and Grabner, M. (2015). Inter- and intra-specific variation in drought sensitivity in Abies spec. and its relation to wood density and growth traits. Agr. For. Meteorol. 214–215, 430–443. doi: 10.1016/j.agrformet.2015. 08.268

Hannrup, B., Cahalan, C., Chantre, C., Grabner, M., Karlsson, B., Le Bayon, I., et al. (2004). Genetic parameters of growth and wood quality traits in Picea abies. Scand. J. For. Res. 19, 14–29 doi: 10.1080/02827580310019536

Ziaco, E., Biondi, F., Rossi, S., and Deslauriers, A. (2014). Intra-annual wood anatomical features of high-elevation conifers in the Great Basin, USA. Dendrochronologia 32, 303–312. doi: 10.1016/j.dendro.2014. 07.006

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Battipaglia, Campelo, Vieira, Grabner, De Micco, Nabais, Cherubini, Carrer, Bräuning, Cufar, Di Filippo, García-González, Koprowski, Klisz, ˇ Kirdyanov, Zafirov and de Luis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# TreeWatch.net: A Water and Carbon Monitoring and Modeling Network to Assess Instant Tree Hydraulics and Carbon Status

#### Kathy Steppe1 \*, Jonas S. von der Crone1 and Dirk J. W. De Pauw1,2

1 Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium, 2 Phyto-IT, Ghent, Belgium

TreeWatch.net is an initiative that has been developed to watch trees grow and function in real-time. It is a water- and carbon-monitoring and modeling network, in which highquality measurements of sap flow and stem diameter variation are collected on individual trees. Automated data processing using a cloud service enables instant visualization of water movement and radial stem growth. This can be used to demonstrate the sensitivity of trees to changing weather conditions, such as drought, heat waves, or heavy rain showers. But TreeWatch.net's true innovation lies in its use of these highprecision harmonized data to also parameterize process-based tree models in real-time, which makes displaying the much-needed mechanisms underlying tree responses to climate change possible. Continuous simulation of turgor to describe growth processes and long-term time series of hydraulic resistance to assess drought-vulnerability in realtime are only a few of the opportunities our approach offers. TreeWatch.net has been developed with the view to be complementary to existing forest monitoring networks and with the aim to contribute to existing dynamic global vegetation models. It provides high-quality data and real-time simulations in order to advance research on the impact of climate change on the biological response of trees and forests. Besides its application in natural forests to answer climate-change related scientific and political questions, we also envision a broader societal application of TreeWatch.net by selecting trees in nature reserves, public areas, cities, university areas, schoolyards, and parks to teach youngsters and create public awareness on the effects of changing weather conditions on trees and forests in this era of climate change.

Keywords: sap flow, stem diameter variation (dendrometer), process-based modeling, vegetation modeling, turgor, hydraulic failure, plant growth, drought

#### INTRODUCTION

Climate change is impacting forests worldwide, threatening biodiversity, and ecosystem function and services (Anderson-Teixeira et al., 2015). Biological consequences of climate change are already apparent (Hughes, 2000), including increased tree mortality through drought, heat stress, insect infestation, and disease outbreaks (Anderegg et al., 2015; Teskey et al., 2015). Just as internationally coordinated forest monitoring initiatives boosted in the 1970s to respond to

Edited by:

Cristina Nabais, University of Coimbra, Portugal

#### Reviewed by:

Tobias Gebauer, University of Freiburg, Germany Zhenzhu Xu, Institute of Botany, Chinese Academy of Sciences, China

> \*Correspondence: Kathy Steppe<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

Received: 10 March 2016 Accepted: 22 June 2016 Published: 05 July 2016

#### Citation:

Steppe K, von der Crone JS and De Pauw DJW (2016) TreeWatch.net: A Water and Carbon Monitoring and Modeling Network to Assess Instant Tree Hydraulics and Carbon Status. Front. Plant Sci. 7:993. doi: 10.3389/fpls.2016.00993

urgent scientific, political, and societal questions related to forest decline in relation to air pollution (Rautio and Ferretti, 2015), large monitoring networks around the globe (**Table 1**) are now investigating forest ecosystem responses to climate change. In particular, understanding the biological response of forests to climate change remains a great challenge, but is critical to biodiversity conservation, and management of ecosystem function and services (Anderson-Teixeira et al., 2015). Data sets from various monitoring initiatives and model forecasts are two essential components to no only understand forest ecosystem responses to climate change, but are also essential to support forest decision makers (Lindner et al., 2014). Adaptation strategies need a framework that includes monitoring and modeling activities as otherwise the forest manager will be grappling in the dark when making decisions (FAO, 2012).

While data from the larger networks (**Table 1**) add to the abundant evidence that forests globally are changing, it remains difficult to identify the mechanisms underlying such changes (Anderson-Teixeira et al., 2015). Focusing on responses of individual trees could single out the relative contributions of these underlying mechanisms. Indeed, trees have been named the 'living laboratories' for climate change responses (Farrell et al., 2015). A tree, like a human, can be viewed as a complex organism with an array of regulatory mechanisms to keep critical systems operating within appropriate bounds and mechanisms to repair damage that may occur when these bounds are exceeded (Anderegg et al., 2012).

In existing networks (**Table 1**), individual tree measurements are, despite their enormous potential, limited to incremental growth, which is conventionally measured every 3–5 years with a tape measure or with simple band dendrometers to track changes in stem circumference. These tree readings can help to start answering major questions about climate change and the potential uptake of CO2 emitted by human activity, but how carbon sequestration and the size of the carbon sink will alter with climate change remains highly uncertain (Popkin, 2015). Because radial stem growth is highly dynamic, speciesspecific, and dependent on environmental factors (see Köcher et al., 2012, 2013), it is a good indicator of tree vitality and of tree responses to environmental stress (Dobbertin, 2005). Modern systems connected to data loggers monitor changes in stem radius (or diameter) at high-frequency time resolution (minute scale), and use these as biological drought and growth indicator (e.g., TreeNet1 , Zweifel, 2016). In addition, a close relationship with net ecosystem productivity, which integrates fluxes over the entire forest ecosystem, has been observed, and although causal explanation for this strong relationship is still fragmentary, it points to a compelling complementarity between both monitoring systems (Zweifel et al., 2010; Gea-Izquierdo et al., 2014).

Continuous time series of stem diameter variations capture diel tree water relations (reflected in reversible shrinkage and swelling) superimposed onto (long-term) irreversible radial stem growth (Steppe et al., 2006; Zweifel et al., 2006; De Swaef et al., 2015). Indeed, changes in diameter result from interactions of water and carbon inside the tree stem (Steppe et al., 2015a). These changes are a great source of tree physiological and ecological information (Zweifel, 2016), which can assist disentangling the mechanisms underlying forest ecosystem responses to climate change. In this paper, we therefore draw on the analogon of an intensive care unit in medical sciences to propose an approach to assess in real-time the tree's trim by considering the tree as a complex organism that transports water from the soil to the atmosphere and sugars from the leaves (sources) to other plant organs (sinks), and whose survival hinges on maintenance of both transport systems. Monitoring equipment on the individual tree level is then used in combination with process-based modeling to translate raw sensor readings into physiological relevant measures, and to simulate key variables, which might be difficult to measure otherwise. We argue that an integrated approach that considers both tree monitoring and process-based modeling is needed to accurately predict forest dynamics in a changing climate.

Smart selection of a certain number of trees in a forest, and of different species in a stand, across different locations and ecosystems over longer time periods, will provide insight into growth, survival and adaptation strategies at the tree and ecosystem scales. The selection of trees and their up-scaling can be inspired by studies that have successfully scaled-up water use from tree to stand level (e.g., Granier et al., 1996; Köstner et al., 1998; Gebauer et al., 2012). Continuous measurements and modeling on the individual tree level are currently lacking in existing networks (**Table 1**) and are largely absent from process-based ecosystem models, while they can provide vital information on internal physiology of tree hydraulic and carbon status complementary to large-scale fluxes measured by eddy covariance and will better inform projections of forest ecosystem responses to climate change.

## THE TREEWATCH.NET INITIATIVE

#### Tree Monitoring

Climate change is expected to drive important changes in tree physiology with manifold but not yet fully understood impacts on forest ecosystem function and services. In this paper, we strongly support the recent calls to focus experimental, observational, and modeling efforts on the tree level to improve our understanding of climate change impacts on forests (Fatichi et al., 2014; Steppe et al., 2015a). We therefore view the tree as a complex organism that needs monitoring equipment to capture a 'heartbeat', which informs us on its actual trim. In contrast to humans, a tree does not have a real heartbeat, but continuous measurements with plant sensors on the tree stem display periodic signals that resemble a human's electrocardiogram (Steppe et al., 2015a). These signals inform us on changes in plant hydraulics and carbon metabolism in xylem and phloem tissues (Steppe et al., 2015a; Zweifel, 2016). From the reviewed methods to quantify real-time water and carbon dynamics within a tree stem (Steppe et al., 2015a), a set of two sensors has currently been selected as basic monitoring equipment: a sap flow sensor and a stem diameter variation sensor (**Figure 1**).

1www.treenet.info

#### TABLE 1 | Important international large-scale forest monitoring networks.

Each initiative is briefly defined, and main goals and approaches have been summarized. The year of foundation and current extent are also given. Networks are sorted from oldest to newest.

ahttp://www.iufro.org

bhttp://www.forestgeo.si.edu (Anderson-Teixeira et al., 2015)

chttp://icp-forests.net

dhttp://icp-forests.net/page/icp-forests-manual

ehttp://fluxnet.ornl.gov

fhttp://www.ilternet.edu

ghttps://www.lternet.edu

hhttp://www.lter-europe.net

ihttps://www.icos-ri.eu

TreeWatch.net, a website built on top of the PhytoSense cloud service, to report instantly on each monitored tree's health status. The unique approach of combining continuous tree measurements with process-based modeling lays the ground for the next-generation global maps displaying direct biological responses of the sampled trees; information which is currently lacking to bridge the gap with meteorology.

When combined with mechanistic modeling (Section "Process-Based Tree Modeling"), these measurements allow revealing the internal tree hydraulics and carbon status. As science evolves, other monitoring equipment, such as acoustic emission sensors (De Roo et al., 2016), may be added or may replace existing ones.

Sap flow is measured with a sap flow sensor, which uses heat to sense water movement in the stem xylem and is typically expressed as sap flow rate (in g h−1 ; Smith and Allen, 1996; Steppe et al., 2010; Vandegehuchte and Steppe, 2013). Accurate estimates of sap flow are essential in our tree monitoring approach to assess

changes in tree hydraulics, internal water storage dynamics, and to quantify whole-tree water use, but also to estimate the tree's carbon budget and stem respiration based on measurements of xylem CO2 transport and stem CO2 efflux (Teskey et al., 2008; Steppe et al., 2015a).

Point dendrometers or linear variable displacement transducers (LVDTs) measure variations in stem diameter (mm) at high temporal resolution (minute scale). The sensor signal simultaneously displays the integrated result of: (i) irreversible radial xylem and phloem growth; (ii) reversible shrinking and swelling of the living stem cells due to changes in internally stored water; (iii) contraction and expansion of dead conducting xylem elements due to the increase and relaxation of internal tensions; and (iv) thermal expansion and contraction of the stem (Daudet et al., 2005; De Swaef et al., 2015). Because of the tight coupling between tree hydraulics and radial stem growth and, hence, carbon metabolism, variations in stem diameter are the second vital component in our tree monitoring approach.

#### Process-Based Tree Modeling

Current spatiotemporal knowledge of climate-forest dynamics is primarily based on simulations by dynamic global vegetation models (DGVMs). Although turgor, or the positive pressure potential in living cells, is the critical component in quantifying growth (Lockhart, 1965; Génard et al., 2001; Steppe et al., 2006), all existing DGVMs simulate long-term tree and forest stand growth using photosynthesized net carbon as source, which is then, according to allometric rules or simplified functional allocation schemes, partitioned among different carbon pools without considering tree hydraulics. Therefore, Fatichi et al. (2014) correctly call for moving from such a carbon source to a more sink-driven vegetation modeling in which water transport and turgor play a key role. As discussed previously (Steppe et al., 2006, 2015a), turgor is not only affecting cell wall expansion or irreversible radial growth (Lockhart, 1965), but also other growth processes, such as cell formation, deposition and assembly of new cell wall material depend on turgor and cell volume (Boyer, 1968; Ray, 1987; Proseus and Boyer, 2006). Because turgor in living tree cells is mainly built-up during night upon refilling of dehydrated tissues, growth processes mainly occur during the night, and are only optimal when tree water status is also optimal (Daudet et al., 2005; Saveyn et al., 2007; Steppe et al., 2015a). If we aspire a better spatiotemporal description of water fluxes together with more realistic scenarios for future climate and the carbon cycle (Friedlingstein et al., 2006; Thornton et al., 2007; Bonan et al., 2011), next generation DGVMs need to include a mechanistic understanding of water and carbon dynamics in xylem and phloem, and their interactions, at the tree level.

During the past few decades, implementation and application of process-based tree models has greatly advanced our knowledge on plant hydraulic functioning and growth (Steppe et al., 2015b). Started from a simple Ohm's law analog model proposed by van den Honert (1948) for steady-state water transport, process-based tree models have greatly improved since, and large efforts have recently being put into the integration of xylem and phloem transport pathways, given the important coupling between hydraulic processes and the transport and allocation of carbohydrates (Höttlä et al., 2009; De Schepper and Steppe, 2010; Hubeau and Steppe, 2015). To further our knowledge of climate change impacts on the forest scale, process-based tree models are likely to become increasingly important.

In our approach, we advocate a combination of processbased tree modeling and continuous measurements at the tree level to better understand impacts of climate change on forests. Given that the current 'heartbeat' tree monitor consists of sap flow and variation in stem diameter, any process-based model that interlinks both processes is a direct candidate for our framework. The history and the current-state-of-the-art of possible candidate process-based models have recently been reviewed (De Swaef et al., 2015). The models we typically consider simulate tree sap flow dynamics, which can be directly linked to variations in stem diameter by using radial flow of water between xylem and phloem (**Figure 1**). The radial water flow causes changes in stem water content via a hydraulic capacitance, which results in changes in turgor, and drives irreversible radial stem growth according to Lockhart's (1965) equation on top of elastic shrinkage and swelling. Steppe et al. (2006) originally developed such a so-called flow and storage model. Of particular interest for our approach is that such models feature essential hydraulic parameters (resistance and capacitance), and enable simulation of vital, but often difficult to measure variables (earlier described turgor, water potential), which all play an important role in hydraulic failure, tree mortality, and, therefore, long-term forest dynamics (Fatichi et al., 2014).

#### Phytosense Cloud Service

Whereas continuous tree measurements, including sap flow and stem diameter variation, have been recognized as promising technology for monitoring tree hydraulics and carbon status (Anderegg et al., 2012; Köcher et al., 2013; De Swaef et al., 2015; Steppe et al., 2015a; Zweifel, 2016), and the use of processbased tree models is expected to get a boost given the recent recommendations on next-generation DGVMs (McDowell et al., 2013; Fatichi et al., 2014), no existing framework combines continuous tree readings with mechanistic modeling in realtime. This is exactly what our approach is aiming at: instant information on tree hydraulics and carbon status using continuous measurements and process-based model simulations.

To optimally combine the continuous tree measurements with the process-based simulation models, and to ensure real-time visualization of the tree's hydraulic function and carbon status, the PhytoSense cloud service is used in our approach (**Figure 1**). This cloud service is the 'brain' behind the commercial plant monitoring system PhytoSense2 and is responsible for real-time data storage, data analysis, data processing, running model simulations and calibrations, and sending out notifications.

All processing on the cloud service is performed automatically so that little or no user interaction is required. A powerful

2Developed by Phyto-IT (Mariakerke, Belgium): http://www.phytosense.net

system based on 'transformations' has been developed for real-time conversion of raw into processed data. A wide range of transformations is available: averaging, cumulating, summing, integrating, filtering, minimum/maximum, and the ability to apply any arbitrary equation to the data. More advanced transformations are also available to calculate sap flow rates in real-time, and to automatically remove disturbances from diameter variation signals. Once defined, transformations are automatically applied each time new data is received. Besides transformations, PhytoSense also allows to run dynamic simulation models in real-time. Although not required, models are typically first implemented in the plant modeling software PhytoSim3 and then converted into optimized code, which can run on the PhytoSense platform. These models can be any set of algebraic and (first order) differential equations [see for instance Steppe et al. (2006) or De Swaef et al. (2015)], and can be automatically recalibrated at certain time intervals using a moving window calibration procedure [see also Steppe et al. (2008)]. This lays the ground for novel stress detection approaches and ecophysiological warning systems, because daily estimates of the calibrated model parameters can now be displayed as time series in real-time from which important tree physiological behavior can be derived. Finally, notifications can be generated when measured or simulated data is below or above a threshold value for a specified amount of time, when a sensor is offline for a specified amount of time or when a model parameter exceeds the appropriate bounds.

PhytoSense provides a flexible API (Application Programming Interface) that allows any internet-connected device to connect to it. Any online data logger can use the API to send data to PhytoSense and custom-build applications or websites can use the API to visualize the available data. This makes the data from the TreeWatch.net trees readily available, which fits the 'Internet of Things' vision of this era.

#### TreeWatch.net and the Way Forward

TreeWatch.net4 is a website built on top of the PhytoSense cloud service (Section "Phytosense Cloud Service"; **Figure 1**). TreeWatch.net originated at the Laboratory of Plant Ecology, Ghent University, Belgium, to show how the combination of tree monitoring and process-based modeling can significantly contribute to instant assessment of stress impacts on tree hydraulics and carbon status. The unparalleled enthusiasm and interest of the COST STReESS5 community in such a measuring/modeling framework gave the impetus to develop TreeWatch.net further as a global water and carbon monitoring and modeling network for advanced research on the dynamic interplay between trees and the regional climate.

Currently, TreeWatch.net monitors beech (Fagus sylvatica L.) and oak (Quercus robur L.) trees in the experimental forest Aelmoeseneie of Ghent University, Belgium. Sap flow is measured with custom-built Sapflow+ sensors (Vandegehuchte and Steppe, 2012) and stem diameter variations are recorded with a point dendrometer (model ZN11-T-WP, Natkon, Switzerland). We plan to gradually extend the network by adding trees across north-south trajectories in different populations in Europe, and in other continents, to profit from a wide climatic gradient going from low temperatures in the northern sites to warm and dry conditions in the southern sites, where tree responses are expected to be temperature- and drought limited, respectively. Trees will be sampled according to a stringent protocol taking into account various tree characteristics (e.g., tree status, tree height, stem diameter, and leaf area), and will be equipped with standardized plant sensors to avoid variability in the collected data due to different sensor types. Sensors connected to data loggers with wireless data transfer and remote control accessibility are used to send the data to the PhytoSense cloud service. The harmonized data offered by TreeWatch.net will be used in an innovative way to parameterize real-time process-based tree models (Section "Process-Based Tree Modeling"), and to run the models to understand tree response to climate change and growth differences across trajectories from underlying water and carbon relationships. Modeling will enable us to put the continuous measurements in a larger context by helping us understand the more general concepts underlying growth and tree hydraulic functioning.

Continuous real-time model simulations of the muchneeded turgor when aspiring growth modeling, but also dynamics in model parameters, including hydraulic resistance and capacitance, are only a few of the opportunities that will be at hand to perform an integrated survey of tree responses to changes in the regional climate. These modeled features should be validated with ground-based data from fieldwork to increase confidence in the model, or to further improve it when discrepancies between modeled and measured data are observed. By visualizing hydraulic features, like hydraulic resistance, we will be the first to show changes in tree hydraulics and vulnerability to drought stress in real-time. The real-time aspect is a much-needed feature because now science relies on off-line, destructively collected vulnerability curves (Choat et al., 2012), which makes assessment indirect and therefore less reliable. In addition, TreeWatch.net aims at displaying pioneering maps with biological tree response to temperature and drought (**Figure 1**), which will be used to bridge the gap between tree functioning and meteorology (weather formation). Weather station data and soil moisture status at the sites, which are needed to interlink tree responses and regional climate, can be either additionally measured or accessed from existing networks (**Table 1**).

The results from TreeWatch.net are expected to spur discussion regarding long-standing assumptions for relationships between fluxes observed at the ecosystem level and the mechanisms responsible. Especially in DGVMs, the use of coarse scale observations and potentially incorrect mechanisms could mislead mitigation and adaptation plans of the future (Hanson and Gunderson, 2009). TreeWatch.net is, therefore, developed to be complementary to the data from these larger networks

3Developed by Phyto-IT (Mariakerke, Belgium): http://www.phyto-it.com/Phyto Sim.shtml

4http://treewatch.net

5COST Action FP1106 – Studying Tree Responses to extreme Events: a SynthesiS: http://www.streess-cost.eu

(**Table 1**) and may help to identify the much-needed mechanisms underlying changes in forests. But the use of TreeWatch.net is not limited to research in natural forest ecosystems only, as it can also be used in cities and urban plantings where trees are known to grow in a 'future climate' and can be used as 'living laboratories' to study plant responses to climate change (Farrell et al., 2015). At present, a maple (Acer pseudoplatanus L.) tree monitored at the Faculty of Bioscience Engineering6 , Ghent University, serves this purpose.

TreeWatch.net is not only powerful in science, but is also able to serve an important educational role by teaching youngsters about the role of trees as regulators of the environment through so-called 'talking forests'7 . In May 2015, the experimental forest of Ghent University was opened as such a 'talking forest', for which a dedicated website8 has been designed using the flexible API of the PhytoSense cloud service. Assisted by experienced nature guides, students from both primary and secondary schools can now be invited to 'listen' to the trees and can find the real-time measurements on their phones or tablets to get insight into the interaction between climate and the forest.

#### CONCLUSION

TreeWatch.net primarily aims at addressing forest and environmental issues that are of concern for our society, and takes the challenge to provide answers to urgent scientific and political climate-change related questions. But because of its intrinsic educational power, one of the long-term dissemination perspectives of TreeWatch.net is that trees are also selected in nature reserves, MAB9 -sites, public areas, cities, university areas, schoolyards, and parks to create global awareness on the effects

#### REFERENCES

of (extreme) weather conditions on trees growing across all continents.

#### AUTHOR CONTRIBUTIONS

KS initiated TreeWatch.net and designed the outline of the initiative. She directed the network toward simultaneous application of continuous tree measurements and process-based modeling. She established the first network in Belgium and added a city tree to demonstrate its potential. She supervises the practical work, the analysis and data interpretation, and she coordinates the modeling activities within the TreeWatch.net framework. DD developed the PhytoSense cloud service and supervises data acquisition, data processing and visualization. He also assists in modeling activities. JvdC assists in daily data inspection, data analysis and data interpretation. He also designed the website. KS wrote the manuscript, JvdC reviewed the existing networks, and JvdC and DD contributed by critically revising the draft. All authors approve the final version to be published.

#### ACKNOWLEDGMENTS

We thank Ghent University for the Science & Society project funding in support of the development of TreeWatch.net, and the Faculty of Bioscience Engineering for the opportunity to equip a faculty tree for UGent's Tipping Po!nt event. We are grateful to our technicians Philip Deman, Geert Favyts and Erik Moerman for their fantastic support with the installation of every tree in the network. We also sincerely thank Ute Sass-Klaassen, as chair of the COST Action FP1106 network STReESS (Studying Tree Responses to extreme Events: a SynthesiS) and the steering committee, for their trust and strong belief in the TreeWatch.net initiative. Our thanks also go to the COST STReESS community for their enthusiastic support and interest in TreeWatch.net. Special thanks goes to Janne Van Camp for the initial cartoons on TreeWatch.net.

6 http://treewatch.net/faculty-of-bioscience-engineering/

7 http://www.flanderstoday.eu/innovation/talking-forest-teaches-youngstersabout-regulating-environment

8 http://www.aelmoeseneiebos.ugent.be/?p=pratendbos

9 Man and the Biosphere

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Steppe, von der Crone and De Pauw. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Repeated Summer Drought and Re-watering during the First Growing Year of Oak (Quercus petraea) Delay Autumn Senescence and Bud Burst in the Following Spring

Kristine Vander Mijnsbrugge1 \*, Arion Turcsán1,2,3, Jorne Maes4 , Nils Duchêne4 , Steven Meeus4 , Kathy Steppe5 and Marijke Steenackers1

1 Department of Forest Genetic Resources, Research Institute for Nature and Forest, Geraardsbergen, Belgium, 2 Department of Biometrics and Agricultural Informatics, Corvinus University of Budapest, Budapest, Hungary, 3 Department of Forest Reproductive Material and Plantation Management, Institute of Silviculture and Forest Protection, West-Hungarian University, Sopron, Hungary, 4 Department of Agro- and Biotechnology, School of Technology, Odisee University College, Sint-Niklaas, Belgium, 5 Laboratory of Plant Ecology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

#### Edited by:

Achim Braeuning, University Erlangen-Nuremberg, Germany

#### Reviewed by:

Gerald Moser, Justus-Liebig-University Giessen, Germany Zhenzhu Xu, Institute of Botany – Chinese Academy of Sciences, China

\*Correspondence:

Kristine Vander Mijnsbrugge<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

Received: 20 January 2016 Accepted: 18 March 2016 Published: 31 March 2016

#### Citation:

Vander Mijnsbrugge K, Turcsán A, Maes J, Duchêne N, Meeus S, Steppe K and Steenackers M (2016) Repeated Summer Drought and Re-watering during the First Growing Year of Oak (Quercus petraea) Delay Autumn Senescence and Bud Burst in the Following Spring. Front. Plant Sci. 7:419. doi: 10.3389/fpls.2016.00419 Climate change predicts harsher summer droughts for mid-latitudes in Europe. To enhance our understanding of the putative impacts on forest regeneration, we studied the response of oak seedlings (Quercus petraea) to water deficit. Potted seedlings originating from three locally sourced provenances were subjected to two successive drought periods during the first growing season each followed by a plentiful rewatering. Here, we describe survival and phenological responses after the second drought treatment, applying general linear mixed modeling. From the 441 drought treated seedlings 189 subsisted with higher chances of survival among smaller plants and among single plants per pot compared to doubles. Remarkably, survival was independent of the provenance, although relatively more plants had died off in two provenances compared to the third one with mean plant height being higher in one provenance and standard deviation of plant height being higher in the other. Timing of leaf senescence was clearly delayed after the severe drought treatment followed by re-watering, with two seedlings per pot showing a lesser retardation compared to single plants. This delay can be interpreted as a compensation time in which plants recover before entering the subsequent developmental process of leaf senescence, although it renders seedlings more vulnerable to early autumn frosts because of the delayed hardening of the shoots. Onset of bud flush in the subsequent spring still showed a significant but small delay in the drought treated group, independent of the number of seedlings per pot, and can be considered as an after effect of the delayed senescence. In both phenological models significant differences among the three provenances were detected independent from the treatment. The only provenance that is believed to be local of origin, displayed the earliest leaf senescence and the latest flushing, suggesting an adaptation to the local maritime climate. This provenance also displayed the highest standard deviation of plant height, which can be interpreted as an adaptation to variable and unpredictable weather conditions, favoring smaller plants in drought-prone summers and higher plants in more normal growing seasons.

Keywords: drought, re-watering, oak provenance, seedling, survival, leaf senescence, bud burst, general linear mixed models

## INTRODUCTION

fpls-07-00419 March 28, 2016 Time: 16:13 # 2

Predicted climate change in temperate regions raises concerns about the ability of forest ecosystems to cope with longer and more severe summer drought periods. The intergovernmental panel on climate change (IPCC, 2014) expects not only a raise in mean temperature but also a higher frequency of extreme weather events. Forest vitality will be challenged and forests will become more vulnerable not only in Europe (Lindner et al., 2010), but all over the globe (Choat et al., 2012). For Europe, and more specifically for Belgium, climate projections predict increasing temperatures and irregular precipitation patterns in summer, augmenting the number and the intensity of drought periods (Baguis et al., 2010). A large part of the forests in the lower countries grows on sandy soils which are characterized by a relatively low water holding capacity, making them specifically vulnerable to extreme drought events during the growing season (Van der Werf et al., 2007), implying that drought and heat tolerance will become more critical for tree survival, especially considering additional stresses for trees by competition with others for light, water, and nutrient sources. Among important European tree species, oaks are well-known to be tolerant to drought (Leuschner et al., 2001), having a xeromorphic leaf structure and an adapted root structure that can cope with temporal and spatial variability in soil water and nutrient availability, and displaying an ability to rapidly resume assimilation after periods of water deficiency (Kubiske and Abrams, 1993; Galle et al., 2007; Kuster et al., 2013a,b). For this reason, oaks are put forward as promising candidate tree species to replace drought sensitive species such as beech (Fagus sylvatica) or spruce (Picea abies) on warm and dry sites in Europe (Leuschner et al., 2001). Apart from interspecific differences in degrees of drought tolerance, also among oak species, intraspecific variation may occur among provenances originating from varying growth sites displaying varying responses to drought. Provenances from xeric sites have been suggested to be better adapted to enhanced temperatures and lower water availability than provenances from more humid sites (Bruschi, 2010; Jensen and Hansen, 2010) although Arend et al. (2011) did not detect a correlation between climate at the site of origin and response to drought in different provenances of three European oak species. A reduced aboveground growth pattern with a diminished biomass production, together with a shift toward below-ground root growth, are wellestablished responses to drought in oak species (Broadmeadow and Jackson, 2000; Thomas and Gausling, 2000; Arend et al., 2011; Spiess et al., 2012; Kuster et al., 2013b) whereas the effects on phenology are less thoroughly examined and mainly described as an earlier stop of height growth under dry growing conditions (Jensen and Hansen, 2010; Spiess et al., 2012), also visible in an earlier cessation in secondary (radial) growth (Pflug et al., 2015) and which may show an after effect in the subsequent spring by an advanced bud burst (Kuster et al., 2014).