Disentangling the multi-faceted growth patterns of primary Picea abies forests in the Carpathian arc

Agricultural and Forest Meteorology 271 (2019) 214–224

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Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

Disentangling the multi-faceted growth patterns of primary Picea abies forests in the Carpathian arc

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Jesper Björklunda,b, , Miloš Rydvala, Jonathan S. Schurmana, Kristina Seftigenc,d, Volodymyr Trotsiuka,b,e, Pavel Jandaa, Martin Mikoláša, Martin Dušátkoa, Vojtěch Čadaa, Radek Bačea, Miroslav Svobodaa a

Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, 165 21, Prague, Czech Republic Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Züercherstrasse111, 8903, Birmensdorf, Switzerland c Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden d Georges Lemaître Centre for Earth and Climate Research (TECLIM), Earth and Life Institute, Université Catholiquede Louvain (UCL), Louvain-la-Neuve, Belgium e ETH Zürich, Department of Environmental Systems Science, Institute of Agricultural Sciences, 8092, Zürich, Switzerland b

A R T I C LE I N FO

A B S T R A C T

Keywords: Carpathian arc Dendrochronology Aggregate tree growth model VS-lite forward growth model Superposed epoch analysis Non-linear climate response

A tree’s radial growth sequence can be thought of as an aggregate of different growth components such as age and size limitations, presence or absence of disturbance events, continuous impact of climate variability and variance induced by unknown origin. The potentially very complex growth patterns with prominent temporal and spatial variability imply that our understanding of climate-vegetation feedbacks essentially benefits from the expansion of large tree ring networks into data-poor regions, and our ability to disentangle growth constraints by comparing ring series at multiple scales. In this study, we analyze Central-Eastern Europe’s most substantial assemblage of primary Norway spruce forests found in the Carpathian arc. The vast data set, > 10,000 tree-ring series, is stratified along a prominent gradient in climate response space over four separate landscapes. We integrated curve intervention detection and dendroclimatic standardization to decompose tree growth variance into climatic, disturbance and residual components to explore the behavior of the components over increasingly larger spatial hierarchies. We show that the residual variance of unknown origin is the most prominent variance in individual Carpathian spruce trees, but at larger spatial hierarchies, climate variance dominates. The variance induced by climate was further explored with common correlation analyses, growth response to extreme climate years and forward modeling of tree growth to identify leading modes of climate response, and potentially nonlinear and mixed climate response patterns. We find that the climatic response of the different forest landscapes overall can be described as an asymptotic response to June and July temperatures, most likely intermixed with influence from winter precipitation. In the collection of landscapes, Southern Romania stands out as being the least temperature sensitive and most likely exhibiting the most complicated mixed temperature and moisture limitation.

1. Introduction The current understanding of tree growth and growth drivers, has benefitted greatly from the fact that the annual radial growth of trees is deposited in the stem as a dateable layer of environmental information – enabling reconstructions of historical climates (Fritts, 1976) or the study of climate impact on the ecology of forests (Schweingruber, 1996). Although this unique archive contains dateable environmental information, it is represented by measurements of tree growth as an aggregate of many different components (Cook, 1985). Translations of

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individual tree‘s ring sequences to knowledge of forest level processes requires disentangling physiological and environmental constraints operating over a variety of temporal and spatial scales (Babst et al., 2018). Under optimal conditions, a tree’s growth sequence is predicted to follow an asymptotic sigmoidal curve, reflecting size-based constraints on incremental growth (e.g., Weiner and Thomas, 2001). Growth is limited at first, increasing to an optimum with the developing tree canopy, where after a plateau or even decline is expected (Warren, 1980). This baseline model of growth is altered by temporal variation in

Corresponding author at: Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Züercherstrasse 111, 8903 Birmensdorf, Switzerland. E-mail address: [email protected] (J. Björklund).

https://doi.org/10.1016/j.agrformet.2019.03.002 Received 23 October 2018; Received in revised form 20 February 2019; Accepted 4 March 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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preliminary step to identify leading modes of response. Secondly, we employ a process-based model of intermediate complexity, Vaganov–Shashkin Lite (VS-lite) (Tolwinski-Ward et al., 2011) to identify potentially non-linear and non-stationary climate response patterns in these spruce forests (sensu Tumajer et al., 2017). In conjunction with this we also deploy a Superposed Epoch Analysis (SEA) (Haurwitz and Brier, 1981) run with extreme weather years. The process-based VS-Lite model predominantly “grows” wood using climatology within the growing season and thus the SEA can be a useful complement for the interpretation of responses detected outside the thermal growing season. Moreover, the SEA is used for identification of falsely detected or non-linear climate responses of tree growth.

growing conditions, including competition for light and edaphic resources (Harcombe and Marks, 1978), and the accumulation of damage throughout the life of the tree, for example exposure to biological pests (e.g. Speer et al., 2001; Veblen et al., 1991), non-lethal damage to the stem and crown by heavy winds (White, 1979), fire (Swetnam, 1993) or drought induced cavitation (Bréda et al., 2006). Additionally, death of neighboring trees can rapidly alter competitive interactions and thus permit abrupt, sustained increases in tree growth (e.g., Marshall, 1927; Frelich and Lorimer, 1991). The most prominent source of inter-annual growth variability is weather and climate. Changes in temperature or precipitation can stimulate or hamper growth depending on which climatic conditions are growth limiting (Babst et al., 2013; Charney et al., 2016; St. George and Ault, 2014). In environments where tree growth is under tight climatic limitation, ring widths are hypothesized to exhibit a linear relationship with the constraining climatic factor (Fritts, 1976). However, in many environments, leading climatic constraints are temporally or spatially variable, generating a high likelihood of multiple resource limitation and non-linear growth-climate responses (Breitenmoser et al., 2014; Bunn et al., 2018). In addition to size or age, disturbance and climate, there is also a more enigmatic variation imprinted in the growth described more or less as unique inter-annual variation of unidentified origin (Cook, 1985). The proportion of unique variation is antagonistically coupled to the general climate sensitivity of the trees and, more fundamentally, to the ability of dating tree rings (Stokes and Smiley, 1968). The potentially very complex growth patterns with prominent temporal and spatial variability imply that our understanding of climate-vegetation feedbacks essentially benefits from the expansion of comprehensive tree ring networks into data-poor regions (Büntgen et al., 2013). The Carpathian arc harbors Central-Eastern Europe’s most substantial assemblage of primary Norway spruce (Picea abies (L. H. Karst)) forests (Sabatini et al., 2018), stratified along a prominent gradient in climate response space (Babst et al., 2013). Norway spruce is generally a cold-adapted, drought-sensitive species (Zweifel et al., 2009; Briffa et al. 1992; Lévesque et al. (2013); Zang et al., 2014), and has in Central Europe been reported to be either temperature sensitive (Mäkinen et al., 2003; Büntgen et al., 2007) indistinctive (Björklund et al., 2017) or moving towards drought sensitivity (Briffa et al. (1992); Lévesque et al. 2013; Tumajer et al., 2017). Local analyses of climategrowth interactions have been conducted at the northwestern (Büntgen et al., 2007, 2013Büntgen et al., 2007Büntgen et al., 2007, 2013) and southern (Primicia et al., 2015) extent of the Carpathians, but comprehensive analyses along climate gradients are lacking for this substantive basis of primary forest resources. The multitude of possible trajectories imposed by physiological and often indistinct environmental constraints on growth (Büntgen et al., 2013) emphasizes the need to report how this specific forest ecosystem reacts to baseline biotic and abiotic factors. In this study, we decompose the variance induced from the various growth components of climate, disturbance and undetermined sources in tree-ring measurements of four different landscapes encompassing more than ten thousand core samples across the Carpathian arc, including Slovakia, Ukraine and Romania. The variance decomposition is executed with a suite of methods, inter-connected to partition one growth component at a time and includes, i) disturbance detection and modeling (Druckenbrod, 2005; Druckenbrod et al., 2013) ii) basic dendroclimatic standardization (Fritts, 1976), and iii) a non-iterative application of the signal-free approach to dendroclimatic standardization (Melvin and Briffa, 2008). Subsequently we analyze how the variance of the different growth components is preserved when averaged over different spatial scales – from trees, to plot-, stand- and landscapelevel. Furthermore, we explore the climate response of the “climatechronology” components at landscape level. We first utilize common correlation-based procedures (Fritts, 1976). This approach is used as a

2. Material and methods 2.1. Tree-ring data and landscape description A large network of forest inventory plots was established along the Carpathian arc continuously sampled and resampled during the last decade (https://www.remoteforests.org/). The network includes 531 plots (0.1 ha) of monotypic primary forests of Picea abies (L.) Karst distributed on 40 forest stands located within four landscapes, in Slovakia, Ukraine and Northern and Southern Romania (Fig. 1) (Janda et al., 2019; Meigs et al., 2017). P. abies mountain forests are widespread in the Slovak Low Tatras, Great Fatra and Small Fatra, where they can predominantly be found from 1200 to 1300 m a.s.l. up to the alpine zone (Holeksa et al., 2017). The mean monthly temperature of July is + 12.5 °C, and −5.6 °C in January (http://www.carpatclim-eu. org, Szalai et al., 2013). Climate means are listed in Table 1. The annual sum of precipitation is 1213 mm and 10 cm of snow is on average present 7 months per year. Rainfall peaks in June and July. Most of the Slovakian Carpathians, particularly the High Tartras, have poor soil on granite bedrock. The large tracts of the subalpine spruce forests have not been regularly managed and large parts are under strict protection. Although silviculture is permitted the low productivity and limited access also effectively prevent human impact (Holeksa et al., 2017). The Ukrainian part of the Carpathians extends from the Northwest to Southeast and represents one of the largest remnants of primary montane spruce forest (Hamor et al., 2008). The mean monthly temperature of July is + 15 °C, and −4.6 °C in January. The annual sum of precipitation is around 1300 mm, where at least 10 cm of snow is on average present 5 months per year, rainfall peaks in June and July. The soils in this landscape are characterized by leptosols and albic podzols on sandstone bedrock (Valtera et al., 2013). Generally, the poor access to the landscape has limited forestry and grazing above 1200 m a.s.l. (Trotsiuk et al., 2014). The Eastern Carpathian Mountains of Northern Romania show, similarly to the Ukrainian tracts, little to no evidence of past human impact (Svoboda et al., 2014) and primary closed‐canopy P. abies mountain forests can be found on slopes higher than 1200 m a.s.l. (Popa and Kern, 2009). The mean monthly temperature of July is + 13.1 °C, and −8.3 °C in January (http://www.carpatclim-eu.org, Szalai et al., 2013). The annual sum of precipitation is lower than the other landscapes at 742 mm, 10 cm of snow is on average present 4 months per year, rainfall peaks in June and July, and snow constitutes a major part of the precipitation here. Soils in the study area are very diverse with podzols, ambisols, leptosols and stagnosols (Svoboda et al., 2014) on volcanic (andesites) (Seghedi et al., 2005) and crystalinic (phyllite, gneiss) bedrocks (Balintoni, 1996). The Fagaras Mountains in the South-central Romania here referred to as “Southern Romania”, consist of an uninterrupted 75–80 km long ridge orientated in an east-west direction (Linnell and Kaltenborn, 2016). The mean monthly temperature of July is likely as high as + 18.5 °C, and −0 °C in January. Note that temperatures have been adjusted for with the discrepancy between mean plot elevation at the landscape and mean grid elevation from the CARPATCLIM, using a 215

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Fig. 1. Locations of the 40 forest stands used in this study. The stands are broadly distributed over four landscapes called Slovakia, Ukraine, Northern Romania and Southern Romania. Each stand is made up of ca 10 plots that most-often include > 20 measurement series each.

reference. The mean sampling elevations approximately represents the difference in tree-line elevation among landscapes. The plots, where tree cores were sampled, were established following approaches outlined in Trotsiuk et al. (2016) and Schurman et al. (2018). In the lab, tree cores were mounted and surfaced with razors or sandpaper prior to the measurement of ring-width conducted with a stereomicroscope coupled to a Lintab™ sliding-stage measuring station and recorded with the software TSAP-Win™. The correct dating of the annual ring-widths was verified using the program CDendro™

lapse rate of 0.65 °C/100 m (Barry et al., 2004). The annual sum of precipitation is 977 mm and 10 cm of snow is on average present 5 months per year (note that snow depths are not adjusted). Rainfall peaks in June and July. The geology is mostly metamorphous consisting of crystalline schists (Nedelea and Comănescu, 2011). The slopes are to a large degree forested, with a coniferous zone above the mixed deciduous forests. Also here, the rugged terrain has limited the extent of human impacts although forestry is wide spread (Linnell and Kaltenborn, 2016). The sampling plot elevations can be found in Table 1, where also the CARPATCLIM grid cell elevations are listed for

Table 1 Basic climate description, mean elevation of the trees at each landscape, and a quantification of how similar each growth index is to each respective landscape chronology, a mean tree-to-landscape correlation (TTLr). The climate means are obtained from http://www.carpatclim-eu.org/ (Szalai et al., 2013) at the nearest 0.1° grids (˜10 km) of each plot over the period of 1981–2010. The elevation difference between the mean tree elevation and the mean grid elevation from CARPATCLIM was used to adjust the absolute temperatures with a lapse rate of 0.65 °C/100 m (Barry et al., 2004). Adjusted temperatures are indicated in brackets. Note that the annual precipitation is not adjusted for lapse rate. The elevations are mean elevations for the plots of each landscape and in brackets for mean of all CARPATCLIM grids of each landscape. Carpathian landscape

Temp. ° C (Jan)

Temp. ° C (July)

Precip. mm (Annual)

Mean tree (clim. grid) elevation

TTLr

“S Romania” “N Romania” “Ukraine” “Slovakia”

−3.5 −5.9 −5.5 −5.4

14.9 15.5 14.0 12.7

977 742 1299 1213

1429 1500 1362 1404

0.33 0.41 0.38 0.35

(0.0) (−8.3) (−4.6) (−5.6)

(18.5) (13.1) (15.0) (12.5)

216

(2040) (1122) (1505) (1383)

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subtracted from the original tree-ring width measurements Gseries, where after the f100(Gseries) standardization was applied. This second set of data therefore represents tree-age/size independent ring-width indices presumably without disturbance trends, i.e. [Cseries + Eseries], note that the E term is still present because the disturbance series do not cover the entire Gseries and the subtraction only inflates or deflates the original variance at the overlap between the original series and the disturbance series (i.e. the disturbance correction procedure does not remove high frequency noise). The difference between the first [Cseries + Dseries + Eseries], and the second [Cseries + Eseries] sets of data thus represents an age-independent record of Dseries. The second sets of data were subsequently averaged over the spatial hierarchies, avg[Cseries + Eseries] into Cplot, Cstand or Clandscape chronologies. Note that this procedure is assumed to cancel out the Eplot, Estand or Elandscape variance. These chronologies were subtracted from each CID treated original ‘spatial-hierarchy-membermeasurement’, ([Aseries + Cseries + Eseries] - Cplot, stand or landscape), to produce so-called signal-free measurements [Aseries + Eseries] (sensu Melvin and Briffa, 2008). Rather than continuing the signal-free approach to standardization in full, we simply subtracted the [Aseries + Eseries] from the [Aseries + Cseries + Eseries] data to produce indices of Cseries variance. Subsequently the [Aseries + Eseries] were subjected to the f100(Gseries) standardization to produce Eseries variance. This process was conducted for the 531 plots in the network, for the 40 stands and finally over the four landscapes. At plot level, the minimum replication requirement for each chronology was > 10 trees. The average standard deviation of every growth component C, D and E, but also the overall growth I, was then calculated at each spatial hierarchy (series, plots, stands and landscapes). All the analyses were designed and executed in a Matlab™ computing environment, and a workflow chart illustrating the variance decomposition approach can be found in the Supporting Information (Fig. S1). It is necessary to point out some caveats concerning the methodology of partitioning growth components. Our standardization procedure f100(Gseries) does not have the ability to preserve climatic variance over centennial periods, therefore we do not make any interpretations on these time scales in any of the tree-growth components. The Dseries component is a product of a set of assumptions of how tree growth reacts when exposed to disturbance. Specifically, the CID method is not capable of detecting very brief responses to disturbances (of ca. < 10 years), should they exist, and these are instead attributed to the unique variance component Eseries. In the Dseries component, there will also be a portion of common or climate variance because the CID technique’s disturbance series may contain inter-annual variability. This variance is a product of the heteroschedastic nature of tree-ring width series (Cook, 1985; Cook and Kairiukstis, 1990) – locally faster growth, be it in time or in space, is most often accompanied by a larger standard deviation. The disturbance-corrected tree-ring series are adjusted to have local standard deviations corresponding to the magnitude of the new local average growth. The difference between the raw and the corrected series, thus have inter-annual and decadal variability of both unique and common variance.

(Cybis.se), as well as COFECHA (Holmes, 1983). In total 10,028 dated tree-ring width series were used in the following analyses.

2.2. Chronology development and growth variance decomposition The conceptual linear aggregate model of tree growth (Eq. 1; Cook, 1985) describes the production of each annual growth increment as the sum of five terms, Gt =At +Ct +δD1t +δD2t +Et

(1)

where Gt represents total ring width, At the age- and/or size-related growth trend, and Ct represents the climate variance in year t. δD1t and δD2t are different types of disturbance variance in year t, where δ indicate the potential presence or absence of D1 (endogenous) and D2 (exogenous) disturbance, respectively. In this study we do not distinguish between δD1t and δD2t, i.e. the disturbance origin. Finally, Et is the more or less unique variance of each series, in year t. In most dendroclimate studies the aggregated growth is in practice only decomposed into two components through a process called detrending or standardization (Peters et al., 2015). That is, multi-decadal or lower frequency trends are largely presumed to represent non-climatic variance that is modeled with a mathematical function (Fritts, 1976), where after this function is divided or subtracted from the original measurement according to Eq.2. Iseries = Gseries – f(Gseries), or Iseries = Gseries / f(Gseries),

(2)

Where Iseries is the indexed growth containing only frequency variance higher than the flexibility of the function f(Gseries), considered to be climate related or high frequency noise. Averaging series over an increasing number of tree cores (i.e. Iplot, Istand or Ilandscape), aims at highgrading the climatic signal of a chronology – it dampens the idiosyncratic variation among individual series while preserving the response shared among individuals. However, if there are conflicting co-dominant signals in the averaged series, the variance will be increasingly suppressed over spatial scales, and partly fail at the aim (Buras et al., 2016; Bunn et al., 2017). Slightly more sophisticated standardization methods, such as the signal-free approach to dendroclimatic standardization (Melvin and Briffa, 2008), are able to preserve important lower frequency climate variance superseding the flexibility of the function used. However, this method cannot distinguish climatically induced variance from plot- or stand-synchronized disturbance variance because it preserves common growth patterns regardless of origin. In this study, we created two sets of tree-ring width indices that allowed us to partition data representing Cseries, Dseries and Eseries variance separately. In the first set, the overall age/size related variance was modeled with cubic smoothing splines with a 50% frequency cutoff at 100 years (Cook and Peters, 1981), and subsequently subtracted from Gseries to produce tree-age/size independent dimensionless indices of tree-ring width, i.e. the Iseries contains [Cseries + Dseries + Eseries] variance. This specific model approach is hereafter referred to as f100(Gseries). Rydval et al. (2015) showed that modeling and removing Dseries variance prior to dendroclimatic standardization can yield improved climate reconstructions, and arguably also demonstrate that the Dseries variance can be skillfully modeled. Thus, in the second set, we modeled Dseries variance with the Curve Intervention Detection techniques (CID) (Druckenbrod, 2005; Druckenbrod et al., 2013). The CID method detects disturbance as outliers from a running mean distribution model of the tree-ring time series. In this study we used CID version 1.05 available online as supplementary material of Rydval et al. (2018). This version is able to detect and model both suppressions and releases in tree-ring width data. For a detailed description of the method, we refer to Rydval et al. (2015) and Rydval et al. (2018). The method was used to assess the timing, duration and magnitude of growth attributable to disturbance – in essence, time series of the components [Dseries + Aseries + Eseries] were obtained in this way. These time series were

2.3. Statistical and process-based analyses of the landscape climate chronologies The Clandscape chronologies were pair-wise correlated and the coefficients were regressed against the respective Euclidean landscape distances, hereafter referred to as correlation decay distance (CCD) (sensu Taylor and Openshaw, 1975). Prior to the correlation procedures, the chronologies were detrended with cubic smoothing splines with a 50% frequency cut-off at 15 years (Cook and Peters, 1981). The time-period of complete overlap between landscape chronologies extends from 1860 to 2010 CE. For reference, the same procedure was performed for climate data. Subsequently, all Clandscape chronologies were correlated with 217

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sensitive trees sampled for dendroclimatological purposes (see Nehrbass‐Ahles et al., 2014; Rydval et al., 2018 for sampling design) this is not surprising, since this dataset was randomly sampled in a part of Europe straddling temperature/moisture response space (Babst et al., 2013; Klesse et al., 2018). However, when averaging trees into chronologies, there is a dramatic decline in E variance, even at plot level, which typically consists of only 20–25 trees. This feature has been theorized (Fritts, 1976) and mathematically derived (Wigley et al., 1984) but rarely empirically shown. The high noise level is important for the absolute variance of the C chronologies. Even though the C variance dominates the aggregated growth in this network, the high noise level necessarily compresses the variance of C and implies that biotic and micro-site abiotic interactions mask the potential stress related to climate sensitivity. Moreover, disturbance variance also plays an important role in the aggregated growth in this part of Europe. The climate-to-disturbance variance ratio is 2 at tree level, and increases steadily towards landscape level where it reaches 6 (Fig. 3a). But, even at this large spatial scale (> 2000 tree cores per landscape), disturbance is detectable, and in Northern Romania, this factor is twice as prominent as in the other landscapes (Fig. 3b). Note that in the series level disturbance panel (Fig. 2), it appears as though the average variance for disturbance is larger than for climate. However, this is not the case because disturbance is not detected for all trees, hence the zero variance affects the central tendency.

climate data. Specifically, the CRU TS4.01 mean air temperature and precipitation 0.5° latitude × 0.5° longitude gridded datasets (Harris et al., 2014) for the grid points enveloping the landscape tree-ring data were used, i.e. one temperature and precipitation grid product for each landscape. Moreover, the landscape chronologies were correlated with the standardized precipitation evapotranspiration index (SPEI) (Vicente-Serrano et al., 2013). The SPEI is a commonly used metric of soil moisture balance and useful for interpretation if tree growth is sensitive to drought or pluvial (e.g. Seftigen et al., 2015). Here we used SPEI aggregated over a three-month time-scale (SPEI3). The SPEI data version 2.3 were obtained from the SPEIbase (available at: http://sac. csic.es/spei/database.html) (Beguería et al., 2010). Because higher warm-season temperatures often are associated with lower precipitation (Trenberth and Shea, 2005), partial correlations were computed using the monthly precipitation and SPEI3 variables. The time period covered for all climate correlations was 1901–2010 CE. Moreover, the tree growth was modeled using the VS-Lite forward model, which can indicate if growth is non-linearly related and/or mixed in its response to climate (Tolwinski-Ward et al., 2011). The model is built around the partial growth responses to temperature and moisture, where temperature modulates growth above the temperature threshold T1, and where growth is no longer increased beyond the maximum growth rate at the threshold T2. The parameters M1 and M2 are corresponding parameters for soil moisture derived from precipitation using a simple leaky-bucket model. The interplay between temperature and moisture is further modulated by latitude φ, theoretically representing the available sunlight for photosynthesis. The threshold parameters (T1, T2, M1 and M2) were determined for each landscape with a Bayesian parameter estimator (available as Matlab-file in the supplement of Tolwinski-Ward et al., 2013), calibrated over the full period 1901–2010. All calculations within the VS-Lite model framework were executed in the VS-lite version 2.3 (ftp:// ftp.ncdc.noaa.gov/pub/data/paleo/softlib/vs-lite; Tolwinski-Ward et al., 2011) using default parameterization. The model was run on Clandscape chronologies without additional high-pass filtering. CRU TS4.01 temperature and precipitation were again used as input variables but with a simple mean value adjustment taking the difference in mean elevation of the CRU grid-points and the mean landscape sampling elevations’ in consideration. Following the estimation of climate correlations, significantly (p < 0.05) correlated monthly temperature, precipitation and SPEI3 variables were further investigated with a Superposed Epoch Analysis (SEA) (Haurwitz and Brier, 1981). We retrieved the 15 most extreme positive and negative climate years for each identified significant climate variable and included those in the SEA. The extreme years were determined from untreated climate data to extract absolute extremes, but also on first differenced data to extract consecutive years that exhibit the largest change in weather from one year to the next. The SEA was executed on Clandscape chronologies without additional high-pass filtering. The SEA was expected to more precisely inform if the climate correlations described were falsely detected, non-linear and indicate how or if the climate response was mixed.

3.2. A moisture driven macro climatic divide among landscapes The correlation among chronologies partly follows the intuitive patterns (Taylor and Openshaw, 1975) that nearby landscapes are better correlated than landscapes farther apart (Fig. 4). Interestingly, the Southern Romania landscape chronology is consistently less correlated with the others – Northern and Southern Romania, the most proximal landscapes, are even less correlated (Fig. 4a) than the more distant landscapes of Slovakia and Northern Romania (Fig. 1). When examining the CCD of the different climatic variables (growing season temperature, precipitation and SPEI3), it appears that the CCD of the three northernmost landscapes is most reminiscent of the CCD pattern of temperature. The correlations of the three northern Clandscape chronologies are in fact higher than the corresponding correlations of precipitation and SPEI3, implying that the connection among the Clandscape chronologies is almost certainly driven by a common temperature response. It is also notable that the CCD of SPEI3 describes an overall pattern very similar to the CCD pattern of all the Clandscape chronologies, firmly suggesting that a distinct shift in moisture regime is responsible for the deviation in CCD between Southern Romania and the three northern landscapes. This regime change could drive the observed spatial inter-correlation pattern for at least two reasons, 1) if these landscapes have mixed climate signals, the spatial difference in temporal moisture variations could explain the observed CCD pattern, 2) the different moisture regime in the south may have an effect on the relative mixing configuration of the climate response, and result in a similar effect on the overall CCD. We come back to this later when discussing the climate response results. It is moreover evident that an enhanced disturbance activity or radial growth response to disturbances that were observed in Northern Romania do not have a dampening effect on the climate response at landscape level. At least, the intensity of the disturbance histories do not appear to have created the reduced connection of Southern Romania to the other landscapes, because there, the level of disturbance history is comparable to Slovakia and Ukraine and lower than in Northern Romania. Although disturbance detection and removal generally have positive effects on climate reconstructions (Rydval et al., 2015), this feature mainly regards the multi-decadal time periods. At inter-annual time periods, disturbance intensity does not appear to have a substantial effect on Clandscape chronologies.

3. Results and discussion 3.1. Decomposition of tree growth variance In this study we decomposed tree-ring data into the three major components of the aggregate tree growth model (Cook, 1985), to our knowledge explicitly done here for the first time (Fig. 2). In the Carpathian arc spruce forests, it appears that the tree unique variance or E, is a major source of variance (Fig. 3a). Very localized spatially uncorrelated micro-environmental factors, variations in circuit uniformity and minor disturbances of the trees within the plots decrease the signal-to-noise ratio (SNR), the ratio of the variances of C and E (Cook, 1985), to considerably lower than 1. Compared to highly 218

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Fig. 2. Aggregated tree-ring indices (I), as well as the decomposed growth components C, D and E along the vertical panel axis, plotted according to a set of spatial scales along the horizontal panel axis. (I) Includes indices and chronologies without age-trends. C includes indices and chronologies of common variance i.e. climate induced variance. D includes indices and chronologies of disturbance, and E includes indices and chronologies of more or less unique variance.

According to the VS-lite forward modeling of tree growth, there is a pronounced difference between Southern Romania and the northern landscapes. The growing season is estimated to be somewhat longer (Fig. 6a) and the number of years where moisture enters as a modulator of growth is more than double (Fig. 6b). The response curve of the thermal growing season is dominated by June, July and August with a skew towards August, in contrast to the climate correlation that exhibited a skew towards June. The analysis indicates that the temperature threshold, T2, beyond which growth no longer responds linearly, is reached for a number of years in all landscapes. All Carpathian

3.3. Complex non-linear and mixed responses in Carpathian spruce chronologies The climate correlation analysis showed that the climate responses of the three Northern landscapes are very similar (Fig. 5). Quite prominent positive June-July temperature correlations are accompanied by negative SPEI3 correlations during the summer. We also find a pronounced winter SPEI3 correlation. The climate response of Southern Romania broadly describes a similar pattern but correlations are muted and often insignificant.

Fig. 3. a) Standard deviation development over spatial hierarchy for C, D, E, and I partitioned data and chronologies. b) Standard deviations of the various landscape disturbance chronologies over time (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article). 219

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Fig. 4. Correlation decay distance for landscape level C chronologies in panel a). The dotted line indicates a divide in common climate response between Southern Romania and the Northern landscapes. b) Same as in a) but for instrumental temperature (June-August), c) precipitation (June-August) and d) August SPEI3 for corresponding coordinates.

of a warm summer is actually neutral (Fig. 7). For all landscapes, however, a large positive change in temperature appears to invoke a positive response in tree growth (Fig. S2). The detected negative correlation with summer SPEI3 is not entirely supported by the SEA. Extreme years of SPEI3 do not seem to have any detectable effect. It appears as though the negative relationship of summer SPEI3 is falsely detected and likely mainly driven by the documented negative correlation between precipitation and temperature during the summer season (Trenberth and Shea, 2005; Seftigen et al., 2018). The winter SPEI3 correlation is however supported by the SEA. High SPEI3 values

landscapes would thus exhibit a non-linear response to growing season temperature and a mixed temperature/moisture response. The mixing ratio of the landscapes is however different – the Southern Romania climate response is more inclined towards moisture, where July and August exhibit moisture limitation on average. The SEA was used to further explore non-linearity and causality of the detected leading climate signals (Fig. 7). Again, the growth responses to summer temperatures were determined to be asymptotic: cold summers significantly reduce growth but warm summers only have a weakly positive influence on growth. In Southern Romania the impact

Fig. 5. Monthly climate correlations for previous and current year relative to the year of growth. Southern Romania is represented by the black solid line and the northern landscapes by the shaded grey bands and dashed colored lines (Slovakia, blue; N Romania, red; Ukraine, green). Solid vertical lines indicate the transition from previous year to current year correlations and the vertical light grey bands indicate the June-July-August season. 220

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Fig. 6. a) VS-lite estimation of growth modulated by either temperature in red or moisture in blue. The red and blue fields represent all years, and the solid lines represent the average growth response. b) Modeled ring width in red and the Clandscape chronology ring width in black. The blue vertical lines indicate years where moisture has entered as modulator of growth, and the blue numbers represent the sum of those years. The header of each panel in b) indicate the correlation (r) between the Clandscape chronology and the VS-lite modeled growth and June-July temperatures, respectively.

Büntgen et al., 2007, 2013; Popa and Kern, 2009) accompanied with causal explanations and experiments (Rossi et al., 2008; Hopkins and Huner, 2004), the detected winter SPEI3 signal is less discussed. Abundant winter moisture is positive but only if summer temperatures are average or above average. Precipitation during winter most often falls as snow and is associated with higher temperatures both through the presence of low-pressure systems, and as insulation of the ground, which moreover promotes nutrient mineralization and buffers the risk of dehydration (Oberhuber, 2004). It also provides a moisture reservoir

are almost always accompanied by increased growth for the Northern landscapes but this response is negligible for Southern Romania. However, dry winters do not invoke correspondingly reduced growth (Fig. 7), unlike large negative changes in winter precipitation (Fig. S2). The general climate response of Carpathian spruce forests is multi faceted – most likely mixed and non-linear in its character, with important spatial differences likely driven by local differences in moisture regimes. While the summer temperature response is thoroughly documented elsewhere (Mäkinen et al., 2003; Frank and Esper, 2005; 221

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Fig. 7. Superposed epoch analysis of extreme climate years. In a) the response of Southern Romania is represented by the black solid line, the northern landscapes by the grey shaded area and dashed colored lines (Slovakia, blue; N Romania, red; Ukraine, green) and climate series are presented for the 15 most positive climate years. In b) the same as in a) but for the 15 most negative climate years.

However, the growth model provided by VS-lite is not more skillful than a linear model of June-July temperatures (Fig. 6b) in predicting tree growth. According to the basic correlation analysis, growth is not particularly modulated by summer moisture as indicated by VS-lite. In fact, VS-lite can only model a positive influence from moisture availability, and this was accordingly detected. However, the modeling of tree growth is rather improved if summer moisture is kept out of the equation and by referring only to the non-linear character of the temperature response. Compounding this, VS-lite gives more weight to August temperature than the basic correlation analysis. Winter moisture was not identified as an important influence of growth in Southern Romania, thus even if a prominent storage of moisture would be parameterized in the VS-lite model, it would likely not improve the output. This could, however, have a real influence on the modeling of the northern Clandscape chronologies but appears not to have been included because here the model skill is again lower for VS-lite than for the linear June-July temperature model. Returning to the discussion in the previous section about the origin of the conspicuous CCD pattern, where Southern Romania stands out. It appears that it is the different moisture regime in the south that affects the relative mix of moisture and temperature influence on growth, distorting the CCD of Southern Romania towards the northern landscapes. Southern Romania is likely more dependent on moisture than the northern sites. This can perhaps be traced to the on average higher temperatures at the plots of this landscape (Table 1). For the high altitude Norway spruce forests of the Carpathian arc we can thus identify important spatial differences, where the predominant temperature response of the northern sites become less distinct in the southern

that is released upon thawing in the spring. If the insulation against cold temperatures were the driving mechanism behind the winter moisture correlation, it would be expected that low SPEI3 years, i.e. translated into cold soils, would invoke a stronger response than warm soils. This is because freezing soils may subject the fine root system to damage (Weih and Karlsson, 2002), increase the risk of winter drought embolism (Mayr et al., 2002), as well as hamper mineralization (Schütt et al., 2014) leading to reduced levels of available nutrients during the growing season. Warm soils only have the advantage of providing nutrient availability. Moreover, if snow effects on soil temperatures were behind this winter SPEI3 signal, we would arguably expect a corresponding winter temperature signal. Instead, taking into consideration the importance of warm summer temperatures, we consider winter precipitation as a moisture reservoir to be the most likely hypothesis behind the strength of the detected correlations. We note however that the interpretation of winter moisture is associated with weaker causal evidence compared with the summer temperature interpretation. An abundant moisture reservoir followed by a warm and sunny summer appears to be the ideal set of growing conditions, that is, a mixed moisture and temperature response. However, if summers are too hot, the growth potential may no longer be fully realized and we thus conclude that a non-linear response to summer temperature is present. The only real predictable modulator of growth in Southern Romania appears to be cold summers. The lack of any other detectable climate signal is most likely related to a complex interplay between temperature and moisture, also indicated by the VS-lite modeling (Fig. 6b).

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supported by Svenska Forskningsrådet Formas no. 2014-723 and VC was supported by the Czech Science Foundation, project GACR no. 1913807S.

landscape and perhaps more prone to moisture modulation. Although the three northern landscapes are very similar in their inter-annual growth patterns and exhibit similar climate-growth responses, the observed multi-decadal scale variation of the Clandscape chronologies is rather varied (Fig. S3), further confounding the predictability of growth. The only truly coherent feature is the increase in growth during the most recent decades, also synchronized with increased temperatures and moisture. In combination with this, the profound uncertainties regarding future rainfall patterns (Stocker et al., 2013) further hinder predictions of growth in Carpathian spruce forests. The buffered climate response, mediated by the low signal-to-noise ratio, indicates that change must be rather significant to have a clear directional effect on Carpathian tree growth. The convergent trends of the last decades’ tree growth, temperature and moisture may be a first indication of this.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agrformet.2019.03. 002. References Babst, F., Poulter, B., Trouet, V., Tan, K., Neuwirth, B., Wilson, R., Panayotov, M., 2013. Site‐and species‐specific responses of forest growth to climate across the European continent. Global Ecol. Biogeogr. 22, 706–717. Babst, F., Bodesheim, P., Charney, N., Friend, A.D., Girardin, M.P., Klesse, S., Moore, D.J., Seftigen, K., Björklund, J., Bouriaud, O., Dawson, A., 2018. When tree rings go global: challenges and opportunities for retro-and prospective insight. Quat. Sci. Rev. 197, 1–20. Balintoni, I., 1996. Geotectonica terenurilor metamorfice din Romania. The Babes-Bolyai University, Cluj Napoca, RO. Barry, R., Chorley, R., Barry, R.G., 2004. Weather and climate in middle and high latitudes. Atmosphere, Weather and Climate. Routledge, pp. 253–301. Beguería, S., Vicente-Serrano, S.M., Angulo, M., 2010. A multi-scalar global drought data set: the SPEIbase: a new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 91, 1351–1354. Björklund, J., Seftigen, K., Schweingruber, F., Fonti, P., Arx, G., Bryukhanova, M.V., Cuny, H.E., Carrer, M., Castagneri, D., Frank, D.C., 2017. Cell size and wall dimensions drive distinct variability of earlywood and latewood density in Northern Hemisphere conifers. New Phytol. 216, 728–740. Blarquez, O., Carcaillet, C., 2010. Fire, fuel composition and resilience threshold in subalpine ecosystem. PLoS One 5. https://doi.org/10.1371/journal.pone.0012480. Bréda, N., Huc, R., Granier, A., Dreyer, E., 2006. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann. For. Sci. 63, 625–644. Breitenmoser, P., Brönnimann, S., Frank, D., 2014. 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4. Conclusions In this study we showed that Carpathian spruce trees exhibit substantial growth variance not associated with climate or disturbance. This variance is however quickly diminished when trees are averaged into chronologies. Disturbance is a less prominent component at tree level but more efficiently propagates over spatial scale aggregation and is even detectable at landscape level. The response to climate is the most prevalent variance at larger spatial scales. However, the climate response is complex, the Carpathian Spruce forests exhibit non-linear temperature response to June and July where warm temperatures do not invoke an equally strong response as cold temperatures. Moreover, the temperature response is mixed with influence of moisture limitation, mainly deriving from the winter months. The similarity among climate-induced variability on landscape-level is prominent, however, with one conspicuous exception. Southern Romania stands out as being the least temperature sensitive and most likely exhibiting the most complicated mixed temperature and moisture limitation. We attribute this difference to the distinct difference in moisture variation in the Southern Romania landscape compared to the Northern landscapes that in turn could be a result of warmer temperatures. Future directions of forest growth in areas of non-linear and mixed responses, obscured by substantial micro-environmental variability, are the most difficult to determine. Here we contribute to a better understanding of the climate drivers of wood formation as a function of geographical location in such a complicated region. Ultimately these analyses are greatly needed to improve estimates of forest productivity and responses to ongoing environmental changes. However, more research is needed to determine how forests will behave when pushed beyond historically encountered climatic conditions. Living individuals have some capacity to acclimate, but there will likely be some changes in the genetic composition of the forests (either as inter-species or intraspecies genetic variation) better suited to new growth limitations. Acknowledgments We are grateful for the comments of three anonymous reviewers. We also acknowledge the development and sharing of Matlab-files for data analysis, specifically D. Druckenbrod for making the source code for the Curve intervention detection application, CID version 1.05, available online as supplementary material of Rydval et al. (2018), S. E. Tolwinski-Ward for making the source code for the VS-Lite model (data contribution series # 2010-130) available through the NOAA/NCDC Paleoclimatology Program, and O. Blarquez for making the source code available for the SEA analysis (Blarquez and Carcaillet, 2010). This work was mainly funded by the EXTEMIT-K project financed by OP RDE at Czech University of Life Sciences Prague (CZ.02.1.01/0.0/0.0/15003/0000433). The work was further supported by the Czech University of Life Sciences: MSMT project LTC17055, and MM was supported by the grants IGA no. B09/17 and CIGA no. 20184304. KS was 223

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