Wild Apple Growth and Climate Change in Southeast Kazakhstan

Home , Apple, Malus sieversii, Picea schrenkiana

Article Wild Apple Growth and Climate Change in Southeast Kazakhstan

Irina P. Panyushkina 1,* ID , Nurjan S. Mukhamadiev 2, Ann M. Lynch 1,3, Nursagim A. Ashikbaev 2, Alexis H. Arizpe 1, Christopher D. O’Connor 4, Danyar Abjanbaev 2, Gulnaz Z. Mengdbayeva 2 and Abay O. Sagitov 2

1 Laboratory of Tree-Ring Research, University of Arizona, 1215 W. Lowell St., Tucson, AZ 85721, USA; [email protected] (A.M.L.); [email protected] (A.H.A.) 2 Z.H. Zhiembaev Research Institute of Plant Protections and Quarantine, Almaty 050070, Kazakhstan; [email protected] (N.S.M.); [email protected] (N.A.A.); [email protected] (D.A.); [email protected] (G.Z.M.); [email protected] (A.O.S.) 3 U.S. Forest Service, Rocky Mountain Research Station, Tucson, AZ 85721, USA; [email protected] 4 U.S. Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USA; [email protected] * Correspondence: [email protected]

Received: 31 August 2017; Accepted: 22 October 2017; Published: 26 October 2017

Abstract: Wild populations of Malus sieversii [Ldb.] M. Roem are valued genetic and watershed resources in Inner Eurasia. These populations are located in a region that has experienced rapid and on-going climatic change over the past several decades. We assess relationships between climate variables and wild apple radial growth with dendroclimatological techniques to understand the potential of a changing climate to influence apple radial growth. Ring-width chronologies spanning 48 to 129 years were developed from 12 plots in the Trans-Ili Alatau and Jungar Alatau ranges of Tian Shan Mountains, southeastern Kazakhstan. Cluster analysis of the plot-level chronologies suggests different temporal patterns of growth variability over the last century in the two mountain ranges studied. Changes in the periodicity of annual ring-width variability occurred ca. 1970 at both mountain ranges, with decadal-scale variability supplanted by quasi-biennial variation. Seascorr correlation analysis of primary and secondary weather variables identified negative growth associations with spring precipitation and positive associations with cooler fall-winter temperatures, but the relative importance of these relationships varied spatially and temporally, with a shift in the relative importance of spring precipitation ca. 1970 at Trans-Ili Alatau. Altered apple tree radial growth patterns correspond to altered climatology in the Lake Balkhash Basin driven by unprecedented intensified Arctic Oscillations after the late 1970s.

Keywords: wild fruit wood forest; Malus sieversii; dendrochronology; climate change; Central Asia

1. Introduction The relict fruit wood forests of Inner Eurasia are highly treasured as genetic reserves for cultivated fruit species, especially apple (Malus spp.), and for the ecosystem services that they provide [1–3]. All domesticated apple varieties around the world originate in part from Malus sieversii (M. sieversii)[4–6]. Wild populations of M. sieversii are genetically more diverse than cultivated populations, exhibiting a much wider range of adaptation to drought and temperature extremes and to pathogens [7,8]. M. sieverii (known as Siever’s apple, Asian apple, and Almaty apple) is the dominant species in the relict wild fruit wood forests in southeast Kazakhstan. The natural range of relict fruit wood forests spreads across many mountain ranges of the Tian Shan and Pamyr Mountains through the neighboring countries: Kyrgyzstan and China, as well as Russian Siberia (e.g., Lake Baykal

Forests 2017, 8, 406; doi:10.3390/f8110406 www.mdpi.com/journal/forests Forests 2017, 8, 406 2 of 14

Siberia (e.g., Lake Baykal region). It is believed that mesophyllic flora of the wild fruitwood forests originatedregion). It isin believed the Tertiary that mesophyllic period and flora survived of the wildextinction fruitwood in isolated forests originatedrefugia during in the the Tertiary last Quaternaryperiod and survivedglaciation extinction [9,10]. in isolated refugia during the last Quaternary glaciation [9,10]. The fruitfruit forests forests surrounded surrounded by semi-desert by semi-desert landscapes landscapes are also valuedare also for valued watershed for preservation, watershed preservation,as well-developed as well-developed and interlaced and apple interlaced root systems apple root protect systems mountain protect soils mountain from erosion soils from and erosionlandslides and [ 7landslides] in a region [7] in prone a region to extreme prone to landslide extreme landslide and river and outflow river eventsoutflow [ 11events]. These [11]. forests These forestshave not have been not inventoried been inventoried in any systematic in any systematic fashion, andfashion, the frequency and the frequency and extent and of insectextent outbreaks of insect outbreaksand pathogen and conditionspathogen areconditions unknown, are but unknown, extensive but forest extensive health issuesforest areheal clearlyth issues evident are clearly in that evidentlarge portions in that oflarge tree portions crowns areof tree dead crowns and foliage are dead is sparse and foliage (Figure is1 ).sparse It is unknown (Figure 1). if It this is unknown condition ifis this short-term condition or reflectiveis short-term of long-term or reflective decline. of long-term Suggested decline. causes Suggested of poor forest causes health of poor range forest from healthpost-seral range stage from succession, post-seral other stage natural succession, ecological other process natural such asecological insect outbreaks process or such pathogens, as insect air outbreakspollution, or changingpathogens, climate air pollution, conditions or changing [7,12]. climate conditions [7,12].

(a) (b)

Figure 1. General view of wild apple stand (a) and tree-ring sampling with increment borer (b). Figure 1. General view of wild apple stand (a) and tree-ring sampling with increment borer (b).

Apple has been studied extensively as a cultivated orchard tree, but its ecology in wild forests is Apple has been studied extensively as a cultivated orchard tree, but its ecology in wild forests is less-well studied [13]. Dzhangaliev (2003) provides a comprehensive synthesis of literature through less-well studied [13]. Dzhangaliev (2003) provides a comprehensive synthesis of literature through the the late 1970s, especially as it relates to fruit production, life history traits, and response to extreme late 1970s, especially as it relates to fruit production, life history traits, and response to extreme weather weather events such as frosts, excessive snow, and seasonal drought [7]. Apple reproduces by both events such as frosts, excessive snow, and seasonal drought [7]. Apple reproduces by both seed and seed and root suckering [7], forming stands composed of both independent trees and surculose root suckering [7], forming stands composed of both independent trees and surculose biogroups. biogroups. Self-grafting root systems and the surculose habit enable biogroups to maintain Self-grafting root systems and the surculose habit enable biogroups to maintain dominance and persist dominance and persist on individual sites, resist defoliator damage, and contribute to soil stability. on individual sites, resist defoliator damage, and contribute to soil stability. Though there is an understanding of fruiting response to extreme weather events [7], the Though there is an understanding of fruiting response to extreme weather events [7], relationships between apple tree growth and climate variables are poorly known. Understanding the relationships between apple tree growth and climate variables are poorly known. Understanding how trees respond to the full range of seasonal precipitation and temperature variables is a how trees respond to the full range of seasonal precipitation and temperature variables is a necessary necessary first step for determining if declining forest health is related to ecological succession, pest ﬁrst step for determining if declining forest health is related to ecological succession, pest dynamics, dynamics, climate change, or a combination of various factors. Here we develop tree-ring climate change, or a combination of various factors. Here we develop tree-ring chronologies for wild chronologies for wild apple and assess the relationships between wild apple radial growth and apple and assess the relationships between wild apple radial growth and climate variables. climate variables. Tree rings have long been used to evaluate the environmental and climate impacts on tree growth. Tree rings have long been used to evaluate the environmental and climate impacts on tree Past tree-ring studies from the Tian Shan Mountains demonstrated strong relationships between growth. Past tree-ring studies from the Tian Shan Mountains demonstrated strong relationships climate and spruce and juniper growth, which facilitated reconstructions of summer temperature and between climate and spruce and juniper growth, which facilitated reconstructions of summer temperaturemoisture variability and moisture [14–18 ].variabilit The tree-ringy [14–18]. based The reconstructions tree-ring based indicate reconstructions increased indicate drought increased stress on droughtconifer tree stress growth on conifer in the mountainstree growth of in Central the mountain Asia. Rapids of Central increase Asia. in the Rapid rate of increase warming in occurredthe rate of in warmingthe last two occurred decades, in which the last caused two thedecades, increase which of summer caused aridity the increase [14,17]. Juniperof summer and sprucearidity growing[14,17]. Juniperat the lower and elevationsspruce growing are most at sensitivethe lower to elevations the drought are stress, most while sensitive conifers to the from drought the upper stress, elevation while conifersshowed signiﬁcantlyfrom the upper increased elevation response showed to signific the temperatureantly increased trend [response15–17]. to the temperature trend [15–17].Tree-ring crossdating was recently used to determine stand demography of wild apple forests and the ageTree-ring of old orchards crossdating [19,20 was]. Yet recently to the bestused of to our det knowledge,ermine stand the demography relationship between of wild radialapple growthforests andof apple the age trees of old and orchards climate with[19,20]. dendrochronology Yet to the best of hasour notknowledge, been evaluated. the relationship We collected between tree-ring radial

Forests 2017, 8, 406 3 of 14 samples from the Jungar Alatau (Figure 1) and Trans-Ili Alatau to evaluate the relationships between climate conditions and annually resolved variations of wild apple tree growth in southern Kazakhstan.

2. Materials and Methods

2.1. Study Areas Large contiguous wild apple forests survive in a few locations of Eurasia, including the foothills of the Tian Shan Mountains in southeast Kazakhstan. Wild apple forests cover about 1463 km2 in Kazakhstan [7]. Trees form regular stands in five mountain ranges, with 49 and 25% of the Kazakh relict fruitwood forest area in the Jungar Alatau and Trans-Ili Alatau, respectively, and lesser amounts in the Karatau (12.1%), Talass Alatau (11.7%) and Tarbagatay (2%). Our study sites are located in the Semirechye (also known as Jetisu or Seven Rivers) Region of the Jungar Alatau and Trans-Ili (Figure 2) in areas where M. sieversii was first identified in 1929 [21]. Conservation management of wild apple groves has been active in this region for at least 80 years. Geographically, the Semirechye is the middle part of the Lake Balkhash Basin between the northern margins of Tian Shan Mountains and Saryesik-Atyrau desert. The landscape closely conforms to the Tian Shan mountainous ranges of Kyrgyzstan and the Chinese province of Xinjiang where wild apple growth occur as well.

Figure 2. Map of study area with insert (upper right) showing the Semirechye, southeast Kazakhstan, in Eurasia. Circles mark positions of the Almaty (1) and Panﬁlov (2) weather stations used in the analyses. Stars show ﬁve primary locations of 12 sampled plots. Sample plot statistics are provided in Table S1.

Apple forms relatively pure open to relatively dense stands with understories of Populus tremula, Sorbus tianschanica, Padus racemosa, Crataegus songarica (C. Koch), Armeniaca vulgaris Lamarck, Acer semenovii and Rosa fedtschenkoana. M. sieversii usually occurs on the slopes of wide river valleys and on lower mountain slopes between steppe vegetation and spruce forest (primarily Picea schrenkiana). We sampled trees on 12 plots in ﬁve locations at an elevation range 1200–1600 m a.s.l. of the Trans-Ili Alatau and Jungar Alatua Ranges, Tian Shan Mountains. A full list of the tree-ring sites can be found in Supplementary Materials Table S1. Sampled plots covered a good range of geographical settings, spanning ca. 400 m of elevation, a wide range of aspect, and slope gradients from 7% to 58% (Table S1). Apple tree size varied considerably, with average diameter at root collar ranging from 40 to 130 cm, and diameter at breast height (1.37 m above ground level) ranging from 20 to 130 cm. The height of sampled mature trees varied from 7 to 13 m.

Forests 2017, 8, 406 4 of 14 to 130 cm, and diameter at breast height (1.37 m above ground level) ranging from 20 to 130 cm. The heightForests 2017 of ,sampled8, 406 mature trees varied from 7 to 13 m. 4 of 14

2.2. Weather Data 2.2. Weather Data Instrumental weather data were not available at the study plots, so we used weather data from AlmatyInstrumental (WMO#36870, weather 43°23 data′. N, were 76°93 not′ E) available and Panfilov at the study (WMO# plots, 36859, so we 44°17 used′ weatherN, 80°07 data′ E). fromThe Almaty (WMO#36870,station record 43begins◦230. N,in the 76◦ 93late0 E) 1880s and Panfilovand is the (WMO# longest 36859, recording 44◦17 0stationN, 80◦ 07in0 theE). Theregion. Almaty We usedstation it recordto evaluate begins seasonal in the late weather 1880s patterns and is the and longest to calculate recording climate station response in the region. functions We usedover itthe to 1920–2014evaluate seasonal period. The weather Panfilov patterns weather and station to calculate period climate of observations response functionsis relatively over short the but 1920–2014 typical forperiod. the region, The Panfilov from 1960 weather to 1992. station We period compared of observations Panfilov data is relatively to Almaty short data but to typicaldetermine for theif climate region, patternsfrom 1960 near to 1992. the Jungar We compared Alatau differed Panfilov from data the to Almaty Trans-Ili data Alatau. to determine Both weather if climate stations patterns are at near lower the elevationsJungar Alatau than differed the study from sites the and Trans-Ili located Alatau. on the Bothsteppe-forest weather stationsboundary are (Almaty at lower is elevations at 895 m a.s.l than., andthe studythe Trans-Ili sites and sites located are at on 1490–1540 the steppe-forest m a.s.l. boundary; Panfilov (Almatyis at 953 ism at a.s.l 895., m60a.s.l km., andnortheast the Trans-Ili of the Jungarsites are Alatau at 1490–1540 sites, which m a.s.l. are; at Panfilov 1245–1475). is at 953Generally, m a.s.l., the 60 kmJungar northeast Alatau ofhas the less Jungar precipitation Alatau sites, and slightlywhich are colder at 1245–1475). winters than Generally, the Trans-Ili the Jungar Alatau Alatau. At Almaty, has less precipitationthe average monthly and slightly temperatures colder winters are +24than °C the and Trans-Ili −6 °C Alatau.in July Atand Almaty, January, the respectively, average monthly and annual temperatures precipitation are +24 is◦C near and 650−6 ◦mm.C in The July growingand January, season respectively, at Almaty and (Tmin annual > 5 °C) precipitation is between isApril near and 650 mm.October, The and growing precipitation season at peaks Almaty in ◦ spring(Tmin > (April–May) 5 C) is between with Aprila second and much October, smaller and precipitationpeak in autumn peaks (October–November) in spring (April–May) (Figure with 3). a July–Septembersecond much smaller is the peak hottest in autumnand driest (October–November) period with precipitation (Figure 3less). July–September than 50 mm per is month. the hottest At Panfilov,and driest the period average with monthly precipitation temperatures less than are 50 +24 mm and per −8 month. °C in July At and Panfilov, January, the respectively, average monthly and annualtemperatures precipitation are +24 is and about−8 ◦250C in mm. July Climate and January, conditions respectively, at the weather and annual stations precipitation are warmer is about and dryer250 mm. than Climate at the conditionsstudy sites at due the to weather the 500–800 stations m are elevation warmer difference, and dryer thanbut represent at the study the sites climate due variabilityto the 500–800 of Semirechye m elevation foothills. difference, but represent the climate variability of Semirechye foothills.

Figure 3. 3. ClimagraphClimagraph calculated calculated from from monthly monthly observ observationsations of the of Almaty the Almaty weather weather station station for 1920– for 2014.1920–2014. Distributions Distributions for individual for individual months months are displayed are displayed as box asplots box with plots a withhorizontal a horizontal line at linethe median,at the median, a box aover box overthe interquartile the interquartile range, range, and andplus plus signs signs at atvalues values more more than than 1.5 1.5 times times the interquartileinterquartile range range above above or or below below the the box. box. If ther If theree are areno such no such outliers, outliers, the bracket the bracket marks marks the data the extremes.data extremes.

2.3. Tree-Ring Tree-Ring Data We sampled 10–12 live apple trees onon ca.ca. 0.4 ha plots at 12 sites (Table S1).S1). Increment core samples werewere takentaken at at 1.37 1.37 m (breastm (breast height) height) or lower. or lower. We selected We selected the oldest-appearing the oldest-appearing and relatively and relativelyhealthiest healthiest trees to obtain trees to the obtain longest the possiblelongest possible dendrochronological dendrochronological record. record. Sampled Sampled cores cores were weremounted mounted and sanded and withsanded progressively with progressively ﬁner grits untilfiner individualgrits until cell individual structure wascell observablestructure was [22]. Ring widths were measured on a Lintab stage system with 0.01-mm precision. Samples were crossdated

Forests 2017, 8, 406 5 of 14 visually and the crossdating was verified with correlation analyses of the measured ring widths using COFECHA [23]. Tree-ring index chronologies were developed using standard methodology, whereby crossdated ring-width series are detrended by the ratio method after first fitting the data with a cubic smoothing spline with frequency response 0.50 at a wavelength of 2/3 the sample series length, which preserves at least 50% of ring-width variance [24]. The individual detrended series were combined into standard plot-level chronologies using a bi-weight robust mean using ARSTAN [25]. Variance of the plot-level chronologies was analyzed using cluster analysis [26] in STATISTICA [27] to determine which plot series should be combined in composite chronologies. Individual-tree ring-width series were screened within each cluster and series with weak inter-series correspondence (Pearson correlation less than 95% confidence) or shorter than 55 years were removed. Remaining individual ring index series within each cluster were composited as above using a bi-weight robust mean using ARSTAN. The composite chronologies were used in statistical response functions of climate and ring growth (Table 1).

Table 1. Statistics * of composite tree-ring chronologies representing the wild apple growth at Jungar Alatau and Trans-Ili Alatau. Short series (<50 years) and low-correlated series were removed from the composite chronologies. EPS statistic (Expressed Population Signal) is above 0.85 for the full length of ring records.

Mountain Grid Elevation Number AR Period Length R SD Range Coordinates m a.s.l. of Series Lag1 Lag2 Jungar 45◦ N, 80◦ E 1254–1475 81 1899–2014 116 0.57 0.21 0.10 0.18 Trans-Ili 43◦ N, 77◦ E 1540–1600 42 1886–2014 129 0.59 0.28 0.31 0.14 * R, Correlation of time series in chronology, SD, Standard deviation, AR, Autocorrelation coefﬁcient.

2.4. Statistical Analysis Linear relationships between apple ring indices and monthly precipitation and mean temperatures were estimated using Seascorr, which applies interserial correlations and partial correlations with a Monte Carlo simulation approach to assess correlation significance of primary and secondary weather variables [28,29]. Seascorr calculates correlations between tree-rings and user-specified primary weather variable, and then calculates partial correlations with a second weather variable after controlling the influence of the primary variable. We alternately specified precipitation and temperature variables as primary in multiple Seascorr analyses for different sub-periods in the tree-ring records. The season-length of climate signals in the tree-ring series was identified with monthly moving windows for 56 seasons. Temporal instability of the relationships was estimated with the difference-of-correlation test [30], which divides the interval of climatic observations into two sub-periods. The Seascorr functions were calculated with historical observations of monthly temperature and precipitation from the Almaty station. Time series variations were summarized in the frequency domain by smoothed periodogram spectral analysis [31] and wavelet analysis [32]. The raw periodogram was smoothed with a succession of Daniell filters such that main spectral features emerged with known bandwidth confidence intervals. Confidence intervals were not adjusted for multiple comparisons (e.g., no Bonferroni adjustment). In wavelet calculation, the applied Morlet wavelet (6.0/6) estimates the relative power at a certain scale and a certain time, and summarizes changes in amplitude of or frequency oscillations as a function of time. Self-calibrated Palmer Drought Severity Index (PDSI) downloaded from GHCN-M v.2/v.3 data set (www.climexp.knmi.nl) was used to evaluate variations on the soil moisture at two grid points 43◦ N–77◦ E and 45◦ N–81◦ E[33]. Monthly time series of the Arctic Oscillation index (AO) were downloaded from the NOAA/ESRL website (www.esrl.noaa.gov). The monthly series were averaged for winter (December–February) and spring (March–April) seasons, and smoothed for comparison with climatic instrumental variables. Forests 2017, 8, 406 6 of 14

3. Results

3.1. Apple Growth Variations Individual-tree ring-width chronologies were developed from 12 plots spanning 48 to 129 years in length. Inter-serial correlations were from 0.42 to 0.64 (p < 0.01). Growth patterns from CH and TP were distinct from all other plots (Figure 2) and were excluded from the composite chronologies. Our field observations indicate that they contained hybrids of wild and inter-planted cultivated varieties. The hybrids may reflect selection for specific growth traits not represented in the larger wild population. CH and TP are located in the vicinity of former collective-farm gardens and cultivated apple tree plantations where records indicate the use of hybrids. The Jungar and Trans-Ili plots formed distinct clusters of ring-width variations. While some between-plot differences are evident these differences were not statistically significant (Figure 4). The Jungar cluster includes 7 Forests 2017, 8, 406 6 of 14 plots from the Kok-Jota, Maslov Griva and Lepsy Valleys, and the Trans-Ili cluster includes 3 plots from Turgen Valley (Figure 2). Little variance in apple ring-width variance was associated with slope3. Results and elevation gradients. The tree-ring series from highly correlated plots resulted in two composite tree-ring records, Jungar and Trans-Ili (Figure 5). Length of the Jungar and Trans-Ili chronologies3.1. Apple Growth are 116 Variations and 129 years, respectively. ComparisonIndividual-tree of the ring-width Almaty and chronologies Panfilov instrumental were developed data from show 12 similar plots spanning climate patterns 48 to 129 for years the commonin length. period Inter-serial of record correlations (1960–1992) were (Figure from S2). 0.42 Both to 0.64 stations (p < 0.01). are warm Growth or cold, patterns wet fromor dry, CH in andthe sameTP were years, distinct though from as expected all other plotsspring (Figure is warmer2) and in werePanfilov excluded than in from Almaty. the composite This indicates chronologies. that the twoOur stations field observations are subject indicate to the same that theyregional contained variat hybridsion, and of that wild the and much inter-planted longer instrumental cultivated varieties. record fromThe hybrids Almaty may can reflectbe used selection in the response for specific function growth analyses traits not for represented both Trans-Ili in the and larger Jungar. wild population. CHBoth and TPcomposite are located chronologies in the vicinity show of formerconsiderab collective-farmle annual gardensvariation and in cultivatedapple growth apple and tree longer-periodplantations where variance records changes indicate frequency the use in of the hybrids. late 1970s The (Figure Jungar and5). Wavelet Trans-Ili analysis plots formed confirms distinct that prominentclusters of ring-widthdecadal variability variations. is lost While in someboth chrono between-plotlogies in differences the late 1970s, are evident with a these corresponding differences increasewere not in statistically quasi-biennial significant (2–3 (Figureand 3–44). year) The Jungarvariation. cluster Spectral includes analyses 7 plots fromindicate the Kok-Jota,significant Maslov (α = 0.05)Griva peaks and Lepsy at 11.6 Valleys, year, 7.1 and year the Trans-Iliand 3.3 clusteryear in includes the Jungar, 3 plots and from at 21.8 Turgen year Valley and (Figure6.3 year2 ).in Little the Trans-Ilivariance inring apple variance. ring-width The spectral variance peaks was associated are in agreement with slope with and elevationsignificant gradients. modes of The variability tree-ring seenseries in from winter highly atmospheric correlated oscillations plots resulted such in as two the composite Siberian tree-ringHigh index records, (SH), JungarEast Asian and Trans-IliWinter Monsoon(Figure 5). (EAWM) Length ofand the Arctic Jungar Oscillation and Trans-Ili [34–36]. chronologies are 116 and 129 years, respectively.

FigureFigure 4. 4. DendrogramDendrogram derived derived from from cluste clusterr analysis analysis of of 12 12 plot plot chronologies chronologies of of apple apple ring ring widths widths (Table(Table S1) S1) calculated calculated with with single single linkage linkage of of Ward Ward minimum variancevariance method.method.

Comparison of the Almaty and Panfilov instrumental data show similar climate patterns for the common period of record (1960–1992) (Figure S2). Both stations are warm or cold, wet or dry, in the same years, though as expected spring is warmer in Panfilov than in Almaty. This indicates that the two stations are subject to the same regional variation, and that the much longer instrumental record from Almaty can be used in the response function analyses for both Trans-Ili and Jungar. Both composite chronologies show considerable annual variation in apple growth and longer-period variance changes frequency in the late 1970s (Figure 5). Wavelet analysis confirms that prominent decadal variability is lost in both chronologies in the late 1970s, with a corresponding increase in quasi-biennial (2–3 and 3–4 year) variation. Spectral analyses indicate significant (α = 0.05) peaks at 11.6 year, 7.1 year and 3.3 year in the Jungar, and at 21.8 year and 6.3 year in the Trans-Ili ring variance. The spectral peaks are in agreement with significant modes of variability seen in winter atmospheric oscillations such as the Siberian High index (SH), East Asian Winter Monsoon (EAWM) and Arctic Oscillation [34–36]. Forests 2017, 8, 406 7 of 14 Forests 2017, 8, 406 7 of 14

Figure 5. Composite tree-ring chronologies of wild apples from the Jungar Alatau (red) and Trans-Ili Figure 5. Composite tree-ring chronologies of wild apples from the Jungar Alatau (red) and Trans-Ili Alatau (black). The chronologies correlate signiﬁcantly for common interval 1899–2014, r = 0.30 Alatau (black). The chronologies correlate significantly for common interval 1899–2014, r = 0.30 p < p < 0.05. Thick line is 10-year Tukey ﬁlter smoothed curve emphasizing the decadal variability. 0.05. Thick line is 10-year Tukey filter smoothed curve emphasizing the decadal variability. Sample Sample size of the composite chronologies is shown in Figure S1. Table 1 shows main statistics of the size of the composite chronologies is shown in Figure S1. Table 1 shows main statistics of the composite chronologies. composite chronologies.

Neither chronology incurred persistent growth suppression coincidental to the change in periodicity.periodicity. Radial growth was was low low in in the the 1900s, 1900s, 1920s, 1920s, and and 1940s 1940s in in both both regions, regions, and and in inthe the 1960s 1960s at atJungar. Jungar. The The highest highest observed observed growth growth was wasbetween between 1950–1960. 1950–1960. Temporal Temporal variations variations of annual of annual apple applegrowth growth are more are pronounced more pronounced in the Trans-Ili in the Trans-Ili region. region. General General correspondence correspondence of apple of ring apple growth ring growthvariations variations (r = 0.30, ( rp= < 0.30,0.05 forp < the 0.05 common for the commonperiod 1899–2014) period 1899–2014) suggests suggeststhat one or that more one common or more commonfactors limit factors apple limit growth apple growthacross the across studied the studied 400-km 400-km span, but span, that but local that factors local factors also influence also inﬂuence tree treegrowth. growth.

3.2. Apple Growth Response to Climate The Seascorr analysis analysis indicates indicates that that annual annual variability variability of of apple apple radial radial growth growth is issensitive sensitive to tofall-winter fall-winter temperatures temperatures and and spring spring precipitat precipitationion (Figure (Figure 6).6). Both Both temperature temperature and moisturemoisture significantlysignificantly impactimpact apple treetree growth,growth, yetyet thethe relationshiprelationship betweenbetween thesethese twotwo limitinglimiting factorsfactors in the Trans-IliTrans-Ili AlatauAlatau shows shows a shifta shift ca. 1970ca. 1970 (Table (Table2). The 2). instabilityThe instability is temporally is temporally coincidental coincidental with changes with inchanges periodicity in periodicity of tree-ring of tree-ring indices describedindices described in the section in the above.section above. Growth Growth at Jungar at Jungar for the for entire the recordentire record is negatively is negatively associated associated with spring with (April–May)spring (April–May) precipitation precipitation (Figure (Figure6A). Similarly, 6A). Similarly, growth atgrowth Trans-Ili at overTrans-Ili the entireover periodthe entire of record period is negativelyof record associated is negatively with springassociated precipitation, with spring but isprecipitation, also negatively but associatedis also negatively with warm associated fall-winter with temperature warm fall-winter (Figure 6 B).temperature However, the(Figure Trans-Ili 6B). relationshipsHowever, the areTrans-Ili not stable, relationships and closer are examinationnot stable, and revealed closer examination that the strong revealed negative that association the strong betweennegative radialassociation growth between and warm radial winters growth identified and warm in winters the pre-1970s identified period in the switches pre-1970s to a strongperiod negativeswitches associationto a strong withnegative spring association precipitation with in spring the 1970s, precipitation which aligns in the the 1970s, growth which response aligns in thethe Trans-Iligrowth response with that in of the Jungar Trans-Ili (Figure with6A). that Consequently, of Jungar (Figure the spring 6A). Conseque weather conditionsntly, the spring became weather more andconditions more influential became more to the and apple more ecology influential in recent to the decades. apple Theecology growth in recent rates ofdecades. apple after The ca.growth 1970 changerates of fasterapple fromafter oneca. 1970 year change to another faster (Figure from one5) when year theto another importance (Figure of spring5) when moisture the importance increases. of spring moisture increases.

Forests 2017, 8, 406 8 of 14 Forests 2017, 8, 406 8 of 14

(A) Jungar 1921-2014 0.4

0.2

0.0 Correlation -0.2

* * -0.4 * * * Jul Apr Jan Jun Feb Mar Aug May Oct* Sept Nov* Dec* Aug* Sep* (B) Trans-Ili Trans-Ili 1921-1968 1970-2014 0.4 0.4

0.2 0.2

0.0 0.0 Correlation Correlation -0.2 -0.2

-0.4 -0.4 * ** * * * * Jul Apr Jan Jun Feb Mar Aug Jul May Oct* Sept Aug* Sep* Nov* Dec* Apr Jan Jun Feb Mar Aug May Oct* Sept Nov* Dec* Aug* Sep*

Figure 6. Primary and partial correlation of 3-month seasons calculated in Seascorr program. (A) Figure 6. Primary and partial correlation of 3-month seasons calculated in Seascorr program. (A) TRW TRW index of the Jungar composite chronology with Almaty temperature (T) and precipitation (P) index of the Jungar composite chronology with Almaty temperature (T) and precipitation (P) for for interval 1921–2014 (full period); (B) TRW index of Trans-Ili composite chronology correlated with intervalAlmaty 1921–2014 T and P (full for period);interval 1921–1968 (B) TRW index(early pe ofriod) Trans-Ili and 1970–2014 composite (late chronology period). Black correlated shows with Almatyprimary T and P-variable P for interval and grey 1921–1968 is secondary (early T-variab period)le and (partial 1970–2014 correlation). (late period).For the interval Black shows1921–1968, primary P-variableT is primary and grey variable is secondary and P is secondary T-variable with (partial the same correlation). color-coding Forof climatic the interval variables. 1921–1968, Dash line T is primarymarks variable significant and Plevel is secondaryof correlation with at p the < 0.05, same and color-coding * denotes season of climatic of the highest variables. correlation Dash line at p marks< signiﬁcant0.01. Y level-axis labels of correlation the end month at p

Season Correlation Intervals Test Statistics Full Early Late ΔZ p Jungar March–May P 1921–2014 1921–1967 1968–2014

Forests 2017, 8, 406 9 of 14

Table 2. Strength of seasonal climate signals the composite tree-ring chronology estimated with the instrumental record at Almaty station. Statistics are shown for the full instrumental period and two instrumental periods of equal length, early (1921–1967) and late (1968–2014), as our analyses show that the temporal structure of the signal changed shortly after ca. 1970 and Seascorr splits series into periods of equal length. Furthermore, the Trans-Ili subperiod comparisons assess how the signals behave within the 1921–1967 (early) and 1968–2014 (late) periods. Correlations are computed in Seascorr with precipitation (P) and partial correlations for temperature (T), except one case where the asterisk denotes temperature is primary and precipitation is secondary. ∆Z is the difference between transformed correlations for the early and late periods [29]. The p-value tests the null hypothesis that the population sample correlations for the early and late period are the same. Bold marks ∆Z with signiﬁcant difference between the correlations of investigated subperiods.

Season Correlation Intervals Test Statistics Full Early Late ∆Z p Jungar March–May P 1921–2014 1921–1967 1968–2014 −0.45 −0.37 −0.52 0.19 0.37 Trans-Ili March–May P 1921–2014 1921–1967 1968–2014 −0.33 −0.16 −0.55 0.46 0.03 Trans-Ili Subperiods

* Novemberyear1–January T 1921–1968 1921–1944 1945–1968 −0.47 −0.49 −0.46 −0.04 0.89 March–May P 1970–2014 1970–1992 1993–2014 −0.53 −0.54 −0.55 0.01 0.99

4. Discussion The frequency of annual ring-width variability at both Jungar and Trans-Ili locations has changed after ca. 1970. The Trans-Ili ring widths showed clear evidence for associating this variance shift to the change in climate. Prominent decadal variability was lost in the 1970s, with a corresponding increase in quasi-biennial variation. Tree radial growth in the Trans-Ili region shifted from being primarily negatively associated warm winter temperature prior to ca. 1970 (i.e., warm winters are unfavorable) to being primarily negatively associated with increased spring precipitation after ca. 1970 (i.e., wet spring conditions are unfavorable). Radial growth at Jungar was consistently negatively associated with spring precipitation amounts. Apple radial growth shows high sensitivity to spring precipitation. The negative relationship is most likely related to delayed growth initiation caused by cooler soil temperatures and reduced oxygen availability maintained by excessive spring moisture [7]. The negative effects of excess soil moisture counteract the effects of warming spring ambient temperatures on springtime growth. Growth sensitivity to warmer fall and winter temperatures is harder to explain, especially given that the negative association with winter temperature was diminished at Trans-Ili as winter temperature increased. Possible explanations include (a) effects on spring vegetative phenology and (b) relative changes in variability of winter temperatures and spring precipitation. Springtime apple vegetative phenology has both chilling and heat requirements and is favored by the combined effects of fall/winter cold and spring heat [37–39]. For example, if cooler than normal conditions occur during years with increased spring precipitation, the onset of radial growth could be delayed. The genetic and physiological processes occurring in trees during winter chill accumulation are poorly understood, and chilling requirements and phenological responses of a given species can vary between regions [40,41]. The shift in the climatic response of apple rings from temperature to precipitation in recent decades is only pronounced in the Trans-Ili Alatau (west) region where plot growth conditions are wetter than in Forests 2017, 8, 406 10 of 14 the Jungar Alatau plots. After the 1970s, spring precipitation corresponds with temperature decreases, and a positive trend slows signiﬁcantly until ca. 2002 (Figure 7). The combination of decreased spring precipitation and rapidly warming winter temperatures is concurrent with the shift in apple growth Forestsresponse 2017,to 8, 406 climate, but additional study is necessary to determine the mechanisms involved. 10 of 14

FigureFigure 7. 7. VariationsVariations of of spring spring precipitation precipitation (blue (blue line) line) and and fall-winter fall-winter temperature temperature (red (red line) line) recorded recorded atat Almaty station. Blue Blue line line is precipitation and dashed line is temperature. Consistent discrepancy betweenbetween these variables occurred after 1969 an andd is marked with dashed vertical line.

GreaterGreater variability in in moisture moisture with warmer climate is is associated with with the shift in ring-width variability response from temperature to moisture. Moisture was more consistent prior to 1960 1960 when when fall-winterfall-winter temperature temperature was was the the primary variable associated with tree growth. Variable Variable moisture moisture afterafter ca. ca. 1970, 1970, under under a awarmer warmer regime, regime, could could have have caused caused the the shift shift in inwhich which variable variable tree tree growth growth is primarilyis primarily responsive responsive to precipitation, to precipitation, since since trees trees resp respondond to both to both climatic climatic variables; variables; tree growth tree growth will varywill varywith with the variable the variable exhibiting exhibiting the thegreatest greatest vari variance.ance. This This would would be be especially especially true true if if there there is is a a thresholdthreshold limiting limiting tree response to winter temperatures. WeWe cannot compare site-specific site-specific moisture and te temperaturemperature conditions because because of of the the lack of weather stationstation datadata at at the the study study sites. sites. However, However, the Almatythe Almaty (in the (in Trans-Ili the Trans-Ili but at a lowerbut at elevation)a lower elevation)weather station weather shows station that shows temperature that temperature increases and increases soil moisture and soil availability moisture decreasesavailability as decreases indicated asby indicated PDSI after by 1970 PDSI in after both 1970 winter in andboth spring winter (Figure and spring S2). Although (Figure S2). there Although are obvious there signs are obvious of poor signsforest of health poor inforest that health large portionsin that large of tree portions crowns ofare tree dead crowns and are foliage dead is and sparse, foliage the is onset sparse, of anythe onsetdecline of is any not evidentdecline inis radial not evident growth patternsin radial and growth our data patterns does not and show our a correspondingdata does not long-term show a correspondingsuppression. Lack long-term of a persistent suppression. decline Lack in theof a composite persistent chronologies decline in the is composite not surprising chronologies because we is notanalyzed surprising data because from the we most analyzed vigorous data trees. from the most vigorous trees. RecentRecent changes changes in in climatology of of springtime and and wintertime wintertime in in the the Lake Lake Balkhash Basin Basin are are implicatedimplicated in in the apple growthgrowth observations.observations. Instrumental Instrumental weather weather observations observations show show that that the the climateclimate of of southeastern southeastern Kazakhstan Kazakhstan has has been been warming warming significantly significantly over over the the last last century. century. Fall-winter Fall-winter temperaturetemperature (November–February) (November–February) has has increased increased 3 3 °C◦C (Figure (Figure S2a), S2a), and and the the rate rate of warming does notnot vary vary much across the studied region. Spring Spring temperature temperature (March–May) (March–May) shows shows a a slightly slightly smaller smaller warming trend for the same time period. Since Since th thee 1920s, spring temperature increased increased 2.2 2.2 °C◦C at Almaty (Figure S2b) and 1 °C◦C at Panfilov. Panfilov. The long-term anomalies of winter precipitation indicate decreasing snow depth almost everywhere between 1940 and 1990 [42,43]. Snow depth decreased decreasing snow depth almost everywhere between 1940 and 1990 [42,43]. Snow depth decreased about 8–14 cm below 2000 m a.s.l. in the studied region [42]. Moisture was more consistent prior to about 8–14 cm below 2000 m a.s.l. in the studied region [42]. Moisture was more consistent prior 1960 when winter temperatures were the primary influence on growth variability. Inconsistent to 1960 when winter temperatures were the primary influence on growth variability. Inconsistent moisture after ca. 1970 could have caused the switch to moisture as a driver, especially in the wetter moisture after ca. 1970 could have caused the switch to moisture as a driver, especially in the wetter Trans Ili region (Figure S2c,d). Trans Ili region (Figure S2c,d). Wintertime and springtime precipitation characteristics are spatially variable over Inner Eurasia. Variations of winter snow in the Lake Balkhash Basin appear to be opposite to those in western and southern Siberia [44]. Yet spring precipitation is more homogeneous across Inner Eurasia, with a gradual increase from the northwest to the southeast across China. Variability of winter-spring hydroclimate is controlled by the Arctic Oscillation (AO), and its teleconnections with the Siberian High-Pressure System (SH) and the East Asian winter monsoon (EAWM). The intensity of SH and EAWM were considerably weakened from the late 1970s to 1990s, which was associated

Forests 2017, 8, 406 11 of 14

Wintertime and springtime precipitation characteristics are spatially variable over Inner Eurasia. Variations of winter snow in the Lake Balkhash Basin appear to be opposite to those in western and southern Siberia [44]. Yet spring precipitation is more homogeneous across Inner Eurasia, with

Forestsa gradual 2017, 8 increase, 406 from the northwest to the southeast across China. Variability of winter-spring11 of 14 hydroclimate is controlled by the Arctic Oscillation (AO), and its teleconnections with the Siberian withHigh-Pressure a steady decrease System (SH)of snow and thecover East and Asian warm winterer winters monsoon over (EAWM). Northern The Eurasia intensity and ofincreased SH and intensityEAWMwere of the considerably AO [34,35]. A weakened positive phase from theof AO late is 1970sassociated to 1990s, with whichdecreasing was associatedwinter snow with over a Northernsteady decrease Eurasia of snow[45,46]. cover In andspringtime, warmer wintersa positive over AO Northern represen Eurasiats increasing and increased precipitation. intensity Numericalof the AO [34modeling,35]. A positive and instrumental phase of AO observations is associated indicate with decreasing a strong winter shift snowin the over AO Northern decadal variabilityEurasia [45 in,46 the]. In late springtime, 1980s, which a positive resulted AO in represents decreased increasing snow cover precipitation. over Siberia Numerical for the next modeling decade butand increased instrumental spring observations rainfall in indicateInner Eurasia a strong [44–46]. shift inIn theour AO context, decadal both variability winter precipitation in the late 1980s, and springwhich resultedprecipitation in decreased contribute snow to coverthe increased over Siberia moisture for the variability next decade during but increased the growth spring onset rainfall after thein Inner late 1980s Eurasia (Figure [44–46 S2c,d).]. In our context, both winter precipitation and spring precipitation contribute to theShifts increased seen in moisture periodicity variability of annual during ring-width the growth variability onset after at both the late Jungar 1980s and (Figure Trans-Ili, S2c,d). and in the climaticShifts seen response in periodicity that is primarily of annual associated ring-width with variability ring width at at both Trans-Ili Jungar correspond and Trans-Ili, to changes and in inthe atmospheric climatic response circulation that ispatterns primarily over associated Northern with and ring Inner width Eurasia at Trans-Ili during correspondthe winter and to changes spring periodsin atmospheric [47]. The circulation highest positive patterns AO over anomaly Northern for the and instrumental Inner Eurasia interval during occurred the winter between and spring 1990 andperiods 2000 [ 47for]. both The highestseasons positive(Figure 8). AO Thus, anomaly our tree-ring for the instrumental analyses suggest interval that occurred changes betweenin wild apple 1990 forestand 2000 growth for both may seasons be linked (Figure to changes8). Thus, in our wint tree-ringer-spring analyses climatology suggest driven that changes by the in changes wild apple in decadalforest growth variability may be of linked atmospheric to changes circulation in winter-spring in the Northern climatology Hemisphere. driven by the Implications changes in decadalof our resultsvariability to forest of atmospheric health are circulation unclear. inData the and Northern analyses Hemisphere. presented Implications here were ofdesigned our results to identify to forest climatehealth are variables unclear. and Data patterns and analyses associated presented with here radial were growth, designed not to to identify assess climate disturbance-related variables and declines.patterns associated with radial growth, not to assess disturbance-related declines.

3 Dec-Feb Mar-Apr 2

0 AO Index

-1

-2

-3 1900 1920 1940 1960 1980 2000 2020 Year

Figure 8. ArcticArctic Oscillation Oscillation index index for for winter winter (Decembe (December–February)r–February) and and spring spring (March–April) (March–April) seasons fromfrom 19001900 oror 1950 1950 to to 2016 2016 (black (black and and grey grey bars, bars, respectively). respectively). Decadal Decadal variability variability of the of index the isindex shown is with a 10-year Turkey ﬁlter smoothed curve (black for winter and grey for spring). shown with a 10-year Turkey filter smoothed curve (black for winter and grey for spring).

5. Conclusions Conclusions The M. sieversii tree-ringtree-ring chronologies are are the first ﬁrst developed for wild apple. They characterize spatial–temporal patterns patterns of of apple apple growth growth variability variability over over the the last last century century in in southeast southeast Kazakhstan. Kazakhstan. Growth variability is similar in two mountain ranges located 400 km apart. Growth was greatest in 1950–1965, and leastleast inin 1938–1950.1938–1950. TheThe spectralspectral properties properties of of growth growth variability variability changed changed in in response response to to climate change, with decadal variability dominant before the late 1970s and quasi-biennial (2–3 year) variability prevailing in more recent decades. The change of signal occurred around the same time that the climatic response of apple growth in the Trans-Ili Alatau shifted from one climate variable (negative association with warm winter temperatures) to another (increased negative association with spring precipitation). The apple occupies a wide range of environmental conditions, and radial growth is highly sensitive to the combined effects of both fall/winter temperature and spring precipitation. Thus, wild apple growth patterns are associated with changes of winter and

Forests 2017, 8, 406 12 of 14 climate change, with decadal variability dominant before the late 1970s and quasi-biennial (2–3 year) variability prevailing in more recent decades. The change of signal occurred around the same time that the climatic response of apple growth in the Trans-Ili Alatau shifted from one climate variable (negative association with warm winter temperatures) to another (increased negative association with spring precipitation). The apple occupies a wide range of environmental conditions, and radial growth is highly sensitive to the combined effects of both fall/winter temperature and spring precipitation. Thus, wild apple growth patterns are associated with changes of winter and spring climatology in the Lake Balkhash Basin driven by unprecedented intensiﬁed Arctic Oscillation in winter-spring time after the late 1970s. It is important to be aware of linkages between large-scale atmospheric circulation patterns and regional scales of climate change, and of the forest growth response to the regional climate variability. Drought conditions are obviously important to forest ecology in the arid Central Asia, but spring moisture surpluses also have an important inﬂuence on wild fruit wood forests of mountain foothills and this needs to be better understood.

Supplementary Materials: The following are available online at www.mdpi.com/1999-4907/8/11/406/s1, Figure S1: Number of tree samples averaged into the composite ring chronologies. Red line is Jungar and black line is Trans-Ili series; Figure S2: Trends in variability of temperature and moisture at the studied locations of Jungar Alatau and Trans-Ili Alatau. Plotted instrumental data show (a) Fall-winter temperature (November–February) and (b) Spring temperature (March–May) observed at the Panfilov station (Blue line) in Jungar and the Almaty weather station (Red line) in Trans-Ili; (c) Winter PDSI and (d) Spring PDSI series from grid 44◦ N–80◦ E (Jungar) and grid 43◦ N–77◦ E (Trans-Ili) measuring the variations of soil moisture. Red and blue lines show variables for the full interval. Grey line shows the early period of observations and black line—the late period (see related Seascorr calculation results in Table 2). Solid straight line shows linear regression fit with 95% confidence intervals (dash line); Table S1: Statistics of 12 plots sampled to study wild apple growth across the Jungar Alatau and Trans-Ili Alatau in the southern Kazakhstan. Plot locations are shown in Figure 1. Acknowledgments: The research was support by Science Committee of Ministry of Education and Science of Republic of Kazakhstan (Program 217, Section 1.8 “Efficient use of natural resources, raw materials and products”, award 4165/GF4) and the U.S. Forest Service, Rocky Mountain Research Station Research Joint Venture Agreement 12-JV-11221633-161 with the University of Arizona. We are thankful to field assistance and support to administration and foresters of the Jungar and Trans-Ili Alatau National Parks, and Forestry Committee of the Ministry of Agriculture, Republic of Kazakhstan. Author Contributions: Irina P. Panyushkina, Ann M. Lynch, Nurjan S. Mukhamadiev and Abay O. Sagitov conceived, designed and analyzed the experiment; Christopher D. O’Connor, Nursagim A. Ashikbaev, Danyar Abjanbaev and Gulnaz Z. Mengdbayeva were primarily responsible for field data collection; Alexis H. Arizpe performed crossdating and ring-width measurements and contributed to data interpretation; Irina P. Panyushkina wrote the first draft and made the revisions, Ann M. Lynch and Christopher D. O’Connor completed the final version of manuscript. All authors took a part in the data analysis. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Cornille, A.; Giraud, T.; Smulders, M.J.M.; Roldán-Ruiz, I.; Gladieux, P. The domestication and evolutionary ecology of apples. Trends Genet. 2014, 30, 57–65. [CrossRef][PubMed] 2. Forte, A.V.; Ignatov, A.N.; Ponomarenko, V.V.; Dorokhov, D.B.; Saveleyev, N.I. Phylogeny of the Malus (apple tree) species, inferred from the morphological traits and molecular DNA analysis. J. Genet. 2002, 38, 1150–1160. 3. Maxted, N.; Kell, S. Establishment of a Global Network for the. In Situ Conservation of Crop Wild Relatives: Status and Needs; FAO Commission on Genetic Resources for Food and Agriculture: Rome, Italy, 2009. 4. Duan, N.; Bai, Y.; Sun, H.; Wang, N.; Ma, Y.; Li, M.; Wang, X.; Jiao, C.; Legall, N.; Mao, L.; et al. Genome re-sequencing reveals the history of apple and supports a two-stage model for fruit enlargement. Nat. Commun. 2017, 8, 249. [CrossRef][PubMed] 5. Velasco, R.; Zharkikh, A.; Affourtit, J.; Dhingra, A.; Cestaro, A.; Kalyanaraman, A.; Fontana, P.; Bhatnagar, S.K.; Troggio, M.; Pruss, D.; et al. The genome of the domesticated apple (Malus × domestica Borkh.). Nat. Genet. 2010, 42, 833–839. [CrossRef][PubMed] 6. Zhukovsky, P.A. Cultivated Plants and Their Ancestors; Nauka: Leningrad, USSR, Russia, 1971; p. 79. Forests 2017, 8, 406 13 of 14

7. Dzhangaliev, A. The wild apple tree of Kazakhstan. Hortic. Rev. 2003, 29, 63–304. 8. Luby, J.; Forsline, P.; Aldwinckle, H.; Bus, V.; Geibel, M. Silk road apples—Collection, evaluation, and utilization of Malus sieversii from Central Asia. HortScience 2001, 36, 225–231. 9. Baranov, V.I. Development of Tertiary Flora on the Territory of USSR; Scientiﬁc Bulletin of Kazan University: Botany, Kazan, Russia, 1954; Volumes 3, p. 362. 10. Beer, R.; Kaiser, F.; Schmidt, K.; Ammann, B.; Carraro, G.; Grisa, E.; Tinner, W. Vegetation history of the walnut forests in Kyrgyzstan (Central Asia): Natural or anthropogenic origin? Quat. Sci. Rev. 2008, 27, 621–632. [CrossRef] 11. Aizen, V.B.; Aizen, E.M.; Melack, J.M. Precipitation, melt and runoff in the northern Tien Shan. J. Hydrol. 1996, 186, 229–251. [CrossRef] 12. World Conservation Monitoring Centre. IUCN Red List of Threatened Plants; Walter, K.S., Gillett, H.J., Eds.; World Conservation Union: Gland, Switzerland; Cambridge, UK, 1998; p. 862. 13. Cornille, A.; Gladieux, P.; Smulders, M.J.M.; Roldan-Ruiz, I.; Laurens, F.; Le Cam, B.; Nersesyan, A.; Clavel, J.; Olonova, M.; Feugey, L.; et al. New insight into the history of domesticated apple: Secondary contribution of the European wild apple to the genome of cultivated varieties. PLoS Genet. 2012, 8, e1002703. [CrossRef] [PubMed] 14. Esper, J.; Schweingruber, F.H.; Winiger, M. 1300 years of climatic history for Western Central Asia inferred from tree-rings. Holocene 2002, 12, 267–277. [CrossRef] 15. Solomina, A.; Maximova, A.; Cook, A. Picea Schrenkian ring width and density at the upper and lower tree limits in the Tien Shan Mountains (Kirgizstan Republic) as a source of paleoclimatic information. Geogr. Environ. Sustain. 2014, 7, 66–79. [CrossRef] 16. Zhang, T.; Yuan, Y.; He, Q.; Wei, W.; Diushen, M.; Shang, H.; Zhang, R. Development of tree-ring width chronologies and tree-growth response to climate in the mountains surrounding the Issyk-Kul Lake, Central Asia. Dendrochronologia 2014, 32, 230–236. [CrossRef] 17. Seim, A.; Omurova, G.; Azisov, E.; Musuraliev, K.; Aliev, K.; Tulyaganov, T.; Nikolyai, L.; Botman, E.; Helle, G.; Dorado Liñan, I.; et al. Climate Change Increases Drought Stress of Juniper Trees in the Mountains of Central Asia. PLoS ONE 2016, 11, e0153888. [CrossRef][PubMed] 18. Feng, T.; Zhang, Y.; Chen, X. Study on the age structure and density of the wild apple forest of Malus sieversii. J. Fruit Sci. 2007, 24, 571–573. 19. Routson, K.J.; Routson, C.C.; Sheppard, P.R. Dendrochronology reveals planting dates of historic apple trees in the southwestern United States. J. Am. Pomol. Soc. 2012, 66, 9–15. 20. Sagitov, A.O.; Mukhamadiev, N.S.; Ashikbaev, N.J.; Mendibaeva, G.J.; Panyushkina, I.P.; Lynch, A.M.; O’Connor, C.D. Toward dendrochronological research of wild apple forests in the southeastern Kazakhstan. In Proceedings of the Innovative and Environmentally Friendly Technologies for Plant Protection, Almaty, Kazakhstan, 24–25 September 2015; pp. 171–175. 21. Vavilov, N. Wild relatives of fruit trees in the Asian part of USSR and the origin of fruit trees. In Works on the Applied Botany, Genetics, Artiﬁcial Selection; USSR: Frunze, The Republic of Kyrgyzstan, 1931; pp. 3–44. 22. Stokes, M.A.; Smiley, T.L. An Introduction to Tree-Ring Dating; University of Chicago Press: Chicago, IL, USA, 1968; p. 73. 23. Holmes, R.L. Computer-Assisted Quality Control in Tree-Ring Dating and Measurement. Tree-Ring Bull. 1983, 43, 69–78. 24. Cook, E.R.; Kairiukštis,¯ L. Methods of Dendrochronology: Applications in the Environmental Sciences; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1992; p. 394. 25. Cook, E.R.; Holmes, R.L. User’s manual for Program ARSTAN. In Tree-Ring Chronologies of Western North America: California, Eastern Oregon and Northern Great Basin with Procedures Used in the Chronology Development Work Including Users Manuals for Computer Programs COFECHA and ARSTAN; Holmes, R.L., Adams, R.K., Fritts, H.C., Eds.; University of Arizona: Tucson, AZ, USA, 1986; pp. 50–65. 26. Ward, J.H. Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc. 1963, 58, 236–244. [CrossRef] 27. StatSoft, Inc. STATISTICA (Data Analysis Software System), Version 8.0. 2007. Available online: www.statsoft.com (accessed on 24 October 2017). 28. Haan, C.T. Statistical Methods in Hydrology, 2nd ed.; Iowa State Press: Ames, IA, USA, 2002; p. 496. Forests 2017, 8, 406 14 of 14

29. Meko, D.M.; Touchan, R.; Anchukaitis, K.J. Seascorr: A MATLAB program for identifying the seasonal climate signal in an annual tree-ring time series. Comput. Geosci. 2011, 37, 1234–1241. [CrossRef] 30. Snedecor, G.W.; Cochran, W.G. Statistical Methods, 8th ed.; Iowa State University Press: Ames, IA, USA, 1989; p. 503. 31. Bloomﬁeld, P. Fourier Analysis of Time Series: An Introduction, 2nd ed.; Wiley: New York, NY, USA, 2000; p. 261. 32. Torrence, C.; Compo, G.P. A Practical Guide to Wavelet Analysis. Bull. Am. Meteorol. Soc. 1998, 79, 61–78. [CrossRef] 33. Harris, I.; Jones, P.D.; Osborn, T.J.; Lister, D.H. Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 Dataset. Int. J. Climatol. 2014, 34, 623–642. [CrossRef] 34. Wu, B.; Wang, J. Winter Arctic Oscillation, Siberian High and East Asian Winter Monsoon. Geophys. Res. Lett. 2002, 29, 3-1–3-4. [CrossRef] 35. Jhun, J.; Lee, E. A New East Asian Winter Monsoon Index and Associated Characteristics of the Winter Monsoon. J. Clim. 2004, 17, 711–726. [CrossRef] 36. D’Arrigo, R.; Jacoby, G.; Wilson, R.; Panagiotopoulos, F. A reconstructed Siberian High index since A.D. 1599 from Eurasian and North American tree rings. Geophys. Res. Lett. 2005, 32.[CrossRef] 37. Kalmykov, S.S. Wild Fruit Trees of Western Tien Shan; Alma-Ata: Almaty, Kazakhstan, 1956; p. 101. 38. Linkosalo, T. Mutual regularity of spring phenology of some boreal tree species: Predicting with other species and phenological models. Can. J. For. Res. 2000, 30, 667–673. [CrossRef] 39. Luedeling, E.; Zhang, M.; Girvetz, E.H. Climatic changes lead to declining winter chill for fruit and nut trees in California during 1950–2099. PLoS ONE 2009, 4, e6166. [CrossRef][PubMed] 40. Luedeling, E.; Brown, P.H. A global analysis of the comparability of winter chill models for fruit and nut trees. Int. J. Biometeorol. 2011, 55, 411–421. [CrossRef][PubMed] 41. Menzel, A.; Sparks, T. Temperature and plant development: Phenology and seasonality. In Plant Growth Climate Change; Blackwell Publ.: Oxford, UK, 2006; pp. 70–95. 42. Aizen, V.B.; Aizen, E.M.; Melack, J.M.; Dozier, J. Climatic and Hydrologic Changes in the Tien Shan, Central Asia. J. Clim. 1997, 10, 1393–1404. [CrossRef] 43. Shiklomanov, A.I.; Lammers, R.B.; Lettenmaier, D.P.; Polischuk, Y.M.; Savichev, O.G.; Smith, L.C.; Chernokulsky, A.V. Hydrological changes: Historical analysis, contemporary status, and future projections. In Regional Environmental Changes in Siberia and Their Global Consequences; Springer: New York, NY, USA, 2013; pp. 111–154. 44. Zuo, Z.; Zhang, R.; Wu, B.; Rong, X. Decadal variability in springtime snow over Eurasia: Relation with circulation and possible inﬂuence on springtime rainfall over China. Int. J. Climatol. 2012, 32, 1336–1345. [CrossRef] 45. Cohen, J.; Saito, K.; Entekhabi, D. The role of the Siberian high in northern hemisphere climate variability. Geophys. Res. Lett. 2001, 28, 299–302. [CrossRef] 46. Panagiotopoulos, F.; Shahgedanova, M.; Hannachi, A.; Stephenson, D.B. Observed Trends and Teleconnections of the Siberian High: A Recently Declining Center of Action. J. Clim. 2005, 18, 1411–1422. [CrossRef] 47. Zhou, S.; Miller, A.J.; Wang, J.; Angell, J.K. Trends of NAO and AO and their associations with stratospheric processes. Geophys. Res. Lett. 2001, 28, 4107–4110. [CrossRef]

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).