In this paper, we introduce the dendroTools R package for studying the statistical 10 relationships between tree-ring parameters and daily environmental data. The core function of 11 the package is the daily_response(), which works by sliding a moving window through daily 12 environmental data and calculating statistical metrics with one or more tree ring proxies. 13 Possible metrics are correlation coefficient, coefficient of determination and adjusted 14 coefficient of determination. In addition to linear regression, it is possible to use nonlinear 15 artificial neural network with Bayesian regularization training algorithm (brnn). The 16 dendroTools provides the opportunity to use daily climate data and robust nonlinear functions 17 for the analysis of climate-growth relationships. Thus, models should be better adapted to the 18 real (continuous) growth of trees and should gain in predictive capabilities. The dendroTools R 19 package is freely available in the CRAN repository. The functionality of the package is 20 demonstrated on two examples, one using mean vessel area (MVA) chronology and one 21 traditional tree-ring width (TRW). 22 Introduction 25 R computer language (R Core Team, 2017) is one of the most powerful platforms for analysing 26 tree-ring data. In recent decades, many useful packages have been developed, which are freely 27 available to the tree-ring community. The dplR package (Bunn, 2008, 2010) is widely used to 28 perform several standard analyses, including interactive detrending, chronology building and 29 the calculation of standard descriptive statistics, and is slowly replacing the traditional software 30 for tree-ring standardisation ARSTAN. The R package treeclim (Zang and Biondi, 2015) 31 provides a unified and fast compilation of established methods, while adding novel functions, 32 such as static and moving bootstrapped response and correlation functions, seasonal correlation 33 analysis, a test for spurious temporal changes in proxy-climate relations, and the evaluation of 34 reconstruction skills. Some other useful R packages developed for tree-ring analysis are 35 dendrometeR (van der Maaten et al., 2016), CAVIAR (Rathgeber et al., 2011), pointRes (van 36 der Maaten-Theunissen et al., 2015), measuRing (Lara et al., 2015), TRADER (Altman et al., 37 2014) and tracheideR (Campelo et al., 2016). These R packages are of significant importance 38 and provide the opportunity of analysing tree-ring data more effectively. Beside R packages, 39 there are also other types of software, that is commonly used for identifying climate signal in 40 an annual tree-ring time series. Two of them are Seascorr (Meko et al., 2011), which runs in 41 MATLAB; and DENDROCLIM2002 (Biondi and Waikul, 2004), a C++ program. 42 The CLIMTREG programme was developed by Beck et al. (2013) and provides the possibility 43 to calculate climate-growth correlations based on daily climate data using variable temporal 44 width together with moving correlations to accommodate for short term as well as long term 45 influe...

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Norway spruce (Picea abies L.) is among the most sensitive coniferous species to ongoing climate change. However, previous studies on its growth response to increasing temperatures have yielded contrasting results (from stimulation to suppression), suggesting highly sitespecific responses. Here, we present the first study that applies two independent approaches, i.e. the non-linear, process-based Vaganov-Shashkin (VS) model and linear daily response functions. Data were collected at twelve sites in Slovenia differing in climate regimes and ranging elevation between 170 and 1300 m a.s.l. VS model results revealed that drier Norway spruce sites at lower elevations are mostly moisture limited, while moist high-elevation sites are generally more temperature limited. Daily response functions match well the pattern of growth limiting factors from the VS model and further explain the effect of climate on radial growth: prevailing growth limiting factors correspond to the climate variable with higher correlations. Radial growth correlates negatively with rising summer temperature and positively with higher spring precipitation. The opposite response was observed for the wettest site at the highest elevation, which positively reacts to increased summer temperature and will most likely benefit from a warming climate. For all other sites, the future radial growth of Norway spruce largely depends on the balance between spring precipitation and summer temperature.

European beech (Fagus sylvatica L.) adapts to local growing conditions to enhance its performance. In response to variations in climatic conditions, beech trees adjust leaf phenology, cambial phenology, and wood formation patterns, which result in different tree-ring widths (TRWs) and wood anatomy. Chronologies of tree ring width and vessel features [i.e., mean vessel area (MVA), vessel density (VD), and relative conductive area (RCTA)] were produced for the 1960–2016 period for three sites that differ in climatic regimes and spring leaf phenology (two early- and one late-flushing populations). These data were used to investigate long-term relationships between climatic conditions and anatomical features of four quarters of tree-rings at annual and intra-annual scales. In addition, we investigated how TRW and vessel features adjust in response to extreme weather events (i.e., summer drought). We found significant differences in TRW, VD, and RCTA among the selected sites. Precipitation and maximum temperature before and during the growing season were the most important climatic factors affecting TRW and vessel characteristics. We confirmed differences in climate-growth relationships between the selected sites, late flushing beech population at Idrija showing the least pronounced response to climate. MVA was the only vessel trait that showed no relationship with TRW or other vessel features. The relationship between MVA and climatic factors evaluated at intra-annual scale indicated that vessel area in the first quarter of tree-ring were mainly influenced by climatic conditions in the previous growing season, while vessel area in the second to fourth quarters of tree ring width was mainly influenced by maximum temperature and precipitation in the current growing season. When comparing wet and dry years, beech from all sites showed a similar response, with reduced TRW and changes in intra-annual variation in vessel area. Our findings suggest that changes in temperature and precipitation regimes as predicted by most climate change scenarios will affect tree-ring increments and wood structure in beech, yet the response between sites or populations may differ.

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