290                                M. MACIAS ET AL.

because climate-growth relationships are not stable through time (Gut...
Figure 1. Map of the Pyrenees showing the distribution of Abies alba (stained,...
292                               M. MACIAS ET AL.

N-S precipitation gradient in the Pyrenees (rain shadow) is stronger t...
                                         Characteristics of the A. alba sampled sites

Site                       ...
294                                M. MACIAS ET AL.

SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH                                      295

                         12         ...
296                               M. MACIAS ET AL.

successive 20-yr periods with a 5-yr lag in order to evaluate the temp...
                                        Sub-regional temperature (T) and precipitation (P) studied datasets.

                                                              TABLE II

and 10 as in Figure 1). Second, we built a long clim...

                                                                     TABLE III
Chronology characteristics: express...

well as for a pair within the Main Range (sites 1 vs. 9 = ...
302                                                           M. MACIAS ET AL.

                  70                     ...

                      0.8                    ...
304                                                              M. MACIAS ET AL.

SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH                                 305

Figure 7. (A). Recent trends (slope of...
306                               M. MACIAS ET AL.

    Table IV summarizes the general climate response for all sites bas...
Correlation (C) and response (R) functions based on bootstrap correlation and orthogonal regression on residual c...
308                                                          M. MACIAS ET AL.

               Regional c...

                                   4. Discussion

310                               M. MACIAS ET AL.

Main Range chronologies, which showed an increase in the length of pre...
SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH                                  311

with different phases of the North Atlan...
312                                       M. MACIAS ET AL.

Brubaker, L. B.: 1986, ‘Responses of tree populations to clima...
SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH                                     313

Jalut, G.: 1988, ‘Les principales eta...
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  1. 1. INCREASING ARIDITY IS ENHANCING SILVER FIR (ABIES ALBA MILL.) WATER STRESS IN ITS SOUTH-WESTERN DISTRIBUTION LIMIT MARC MACIAS1,2,3 , LAIA ANDREU2 , ORIOL BOSCH2 , J. JULIO CAMARERO4 ´ and EMILIA GUTIERREZ2 1 Department of Geology, University of Helsinki, Gustaf H¨ llstr¨ minkatu 2 (P.O. Box 64). FI-00014 a o University of Helsinki, Finland E-mail: 2 Departament d’Ecologia, Universitat de Barcelona, Avgda. Diagonal, 645. Barcelona 08028, Catalonia, Spain 3 Finnish Forest Institute, Rovaniemi Research Station, Etel¨ ranta 55. 96300-Rovaniemi, Finland a 4 Unidad de Recursos Forestales, Centro de Investigaci´ n Agroalimentaria, Gobierno de Arag´ n, o o Apdo. 727, Zaragoza 50080, Arag´ n, Spain o Abstract. Tree populations located at the geographical distribution limit of the species may provide valuable information about the response of tree growth to climate warming across climatic gradients. Dendroclimatic information was extracted from a network of 10 silver-fir (Abies alba) populations in the south-western distribution limit of the species (Pyrenees, NE Iberian Peninsula). Ring-width chronologies were built for five stands sampled in mesic sites from the Main Range in the Pyrenees, and for five forests located in the southern Peripheral Ranges where summer drought is more pronounced. The radial growth of silver-fir in this region is constrained by water stress during the summer previous to growth, as suggested by the negative relationship with previous September temperature and, to a lesser degree, by a positive relationship with previous end of summer precipitation. Climatic data showed a warming trend since the 1970s across the Pyrenees, with more severe summer droughts. The recent warming changed the climate-growth relationships, causing higher growth synchrony among sites, and a higher year-to-year growth variation, especially in the southernmost forests. Moving- interval response functions suggested an increasing water-stress effect on radial growth during the last half of the 20th century. The growth period under water stress has extended from summer up to early autumn. Forests located in the southern Peripheral Ranges experienced a more intense water stress, as seen in a shift of their response to precipitation and temperature. The Main-Range sites mainly showed a response to warming. The intensification of water-stress during the late 20th century might affect the future growth performance of the highly-fragmented A. alba populations in the southwestern distribution limit of the species. 1. Introduction Tree populations located at the limit of the species geographical distribution may be responding more dramatically to climate change than those at the core of the range (Brubaker, 1986; Gaston, 2003). Several authors have noted a greater sensitivity of radial growth in response to climate variability in marginal populations than in those found at the main range of species (Schulman, 1954; Fritts, 1976; Villalba et al., 1997; Biondi, 2000). This spatial variability interacts with temporal variability Climatic Change (2006) 79: 289–313 DOI: 10.1007/s10584-006-9071-0 c Springer 2006
  2. 2. 290 M. MACIAS ET AL. because climate-growth relationships are not stable through time (Guti´ rrez et al., e 1998; Tardif et al., 2003). Several tree species, such as Abies alba Mill., Pinus sylvestris L., Pinus uncinata Ram., Fagus sylvatica L., etc., meet in the Iberian Peninsula their southern latitudi- nal limit of distribution. Silver fir (A. alba) main distribution area is found in Central Europe. Silver-fir populations located in the south side of the main Pyrenean axis (hereafter Main Range) and nearby, less elevated ranges further south from the Main Range (hereafter Peripheral Ranges), constitute the south-western limit of the species (Jalas et al., 1999; Figure 1). It is a highly-fragmented distribution area, since most of these populations are very small (usually less than 50 ha) and far from each other. A. alba stands are usually found on the highest quality and productivity sites in the Pyrenees, where they form dense monospecific stands or coexist with F. sylvatica in the westernmost locations (Vigo and Ninot, 1987; Blanco et al., 1997). These zones may experience summer drought but receive abundant precipitation during spring and autumn (Aussenac, 2002). In the study area, A. alba grows in humid sites on north-facing, shady slopes with relatively deep soils, although often very stony, where the risk of severe water stress in summer is lower than in the surrounding areas often dominated by P. sylvestris forests. A. alba populations may also appear in valley bottoms, but always at elevations above 1200 m.a.s.l. Silver fir has been historically subjected to regular logging in the Pyrenees, in some cases up to the late 1970s, when it was no longer used as a source of timber (Kirby and Watkins, 1998; Cabrera, 2001). Managed forests pose a set of problems in the standardization process of ring-width series, which is a critical step in dendroclimatology and dendroecology (Fritts, 1976; Cook et al., 1990). However, dendroecological studies from these marginal stands are valuable tools to assess the growth-climate relationships at the limit of the species distribution area, where recent decline episodes have been described (Camarero, 2001). To extract the climatic signal contained in tree-ring series from stands disturbed by local disturbances such as logging, new methodological approaches must be used. In addition, many trees should be sampled across a large geographical area to obtain a reliable growth pattern related to the regional climatic signal. Orography and atmospheric circulation patterns create a rain shadow in the Southern Pyrenees. Most of the rain carried by low pressure systems coming from North Atlantic falls north of the Continental Divide (Plana, 1985). The rain shadow especially affects Peripheral Southern Ranges, which receive less precipitation than Pyrenean Main Range (Allu´ , 1990). As a whole, climate in the area has a e strong Mediterranean influence and summer drought is not uncommon (Figure 1). This influence decreases westward. Easternmost Ranges get extra precipitation because cyclone formation is enhanced in the NW Mediterranean region due to the combined effects of the Pyrenees and the Southern Alps to the westerly flow (Barry, 1992; Cuadrat, 2000). Also in this region, Mediterranean moisture enhances the growth of summer thunderstorms which soften summer drought. Overall, the
  3. 3. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH Figure 1. Map of the Pyrenees showing the distribution of Abies alba (stained, Blanco et al., 1997). Note the scarce and scattered presence of silver fir in Spanish Territory (S of the dotted line). Numbers are locations of the sites, displayed as in table I. Letters are meteorological stations used to build the sub-regional series of temperature and precipitation (circles). Jacetania/G´ llego: (a). Candanch´ , (b). Canfranc, (c). Sallent de G´ llego; Jacetania/Hoya: a u a n´ n ` (d). Jaca, (e). Sabi˜ anigo, (f). Bolta˜ a, (g). Nocito; Pallars: (h). Estany Gento, (i). Cabdella, (j). Estany de Sant Maurici, (k). Espot, (l). Esterri d’Aneu, (m). Tavascan; Alt Urgell: (n). Oliana, (o). Organy` , (p). Adrall; Cerdanya: (q). Port´ , (r). Puigcerd` , (s). La Molina; Ripoll` s: (t). N´ ria, (u). Ribes de Freser, a e a e u (v). Ripoll, (x). Vallter; Montseny: (y). Tur´ de l’Home, (z). Cardedeu and aa. Girona. Square: Pic du Midi station. Climatic diagrams are shown next to o each sub-regional series of temperature and precipitation. 291
  4. 4. 292 M. MACIAS ET AL. N-S precipitation gradient in the Pyrenees (rain shadow) is stronger than the W-E Atlantic-Mediterranean gradient, as evidenced by climatic and floristic data (L´ pez o Vinyallonga, 2004). The climatic trends in the Mediterranean Basin during the last 50 years were characterized by a rise of mean temperature (2–4 ◦ C), and an increase in both the fre- quency and intensity of severe droughts (IPCC, 2001). Piervitali et al. (1997) noted a 20% decrease in total precipitation between 1951 and 1995 in the Western Mediter- ranean Basin. In the Iberian Peninsula, the 1980–1995 period was characterized by intense droughts, which caused the decline of several woody species (Pe˜ uelas n et al., 2001). In the Central Pyrenees, mean annual temperature has increased by 0.83 ◦ C at the Pic du Midi meteorological station (2862 m.a.s.l). between 1882 and 1970 (B¨ cher and Dessens, 1991; Dessens and B¨ cher, 1995). For Western-Europe u u mountains, Diaz and Bradley (1997) reported a similar strong warming trend since the 1940s, resulting in the latest decades being much warmer than any other period of the instrumental records. In conclusion, climate in the Iberian Peninsula during the 20th century has been characterized by exceptionally high temperatures with a great interannual variability within the context of the last 500 years (Manrique and Fern´ ndez-Cancio, 2000; Camarero and Guti´ rrez, 2004). a e The strong climate warming detected in the Pyrenees during the 20th century involves increasing aridity and should be detected among species sensitive to water stress such as A. alba. Specifically, we hypothesize that silver-fir forests located in the southern Peripheral Ranges have experienced a more intense water stress in response to warming than stands located in the Main Range, where precipitation is higher than in the former sites. The climate-growth relationship might indicate when and where water stress is increasing. The objectives of this study were: (i) to assess the sensitivity of silver-fir populations located in contrasting sites (Peripheral vs. Main Ranges) in response to the regional climate warming observed in the Pyrenees, and (ii) to analyse the spatio-temporal variability of radial growth in these two contrasting groups of sites. To achieve this aim we have established the first dendrochronological network of A. alba in the south-western limit of the species distribution (NE Iberian Peninsula). 2. Material and Methods Ten silver-fir chronologies were produced for the present work (Table I, Figure 1). We sought for a homogeneous spatial distribution along the E-W axis of the Pyre- nees when selecting those sites where old trees could be found. In each stand, 10–17 trees were selected and cored. At least two cores per tree were extracted at 1.3 m using an increment borer (28–40 cores per site). The cores were visually cross-dated following Yamaguchi (1991). Then, ring widths were measured to the nearest 0.01mm using a semiautomatic ANIOL measuring device (Aniol, 1983) and the resulting series were checked statistically using the program COFECHA (Holmes, 1983).
  5. 5. TABLE I Characteristics of the A. alba sampled sites Site Location Latitude(N) Longitude Span Elevation(m) Aspect 1 Aztparreta MAIN RANGE 42◦ 90 0◦ 78 W 1749–1999 1400 NE 2 Pe˜ a Oroel n PERIPHERAL S RANGES 42◦ 55 0◦ 53 W 1889–2000 1650 NNW 3 Guara PERIPHERAL S RANGES 42◦ 30 0◦ 20 W 1893–1999 1500 NNE 4 Conangles MAIN RANGE 42◦ 63 0◦ 78 E 1578–1999 1800 N 5 La Mata de Val` ncia e MAIN RANGE 42◦ 63 1◦ 06 E 1767–1999 1750 N 6 Boavi MAIN RANGE 42◦ 68 1◦ 32 E 1878–1999 1470 N 7 Boumort PERIPHERAL S RANGES 42◦ 20 1◦ 20 E 1804–1999 1583 N 8 Moixer´o PERIPHERAL S RANGES 42◦ 31 1◦ 81 E 1852–1999 1680 NW 9 Setcases MAIN RANGE 42◦ 39 2◦ 27 E 1777–1999 1750 NE 10 Montseny PERIPHERAL S RANGERS 41◦ 77 2◦ 43 E 1587–1999 1550 N SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 293
  6. 6. 294 M. MACIAS ET AL. 2.1. RADIAL GROWTH CHANGES, STANDARDIZATION AND CHRONOLOGY BUILDING In order to reconstruct and infer the logging history of the studied sites, we calculated the percentage growth change for each individual tree-ring series in all the study sites. To identify growth releases we used the formula proposed by Nowacki and Abrams (1997): GC = 100 * [(M2–M1) / M1] × 100, where GC is the percentage growth change between preceding and subsequent 10-yr ring-width means, and M1 and M2 are the preceding and subsequent 10-year means, respectively. A release or abrupt growth recovery along an individual tree-ring series was defined as any GC > 75 %. We calculated the yearly relative frequency of releases per year for the Main-Range and Peripheral-Range groups of sites. In the case of mesic sites with a long history of logging, the only reliable method able to produce complete chronologies without release signals was to fit a very flexible smoothing spline (Cook and Peters, 1981), although it removed a considerable amount of long– and mid-term climatic signal from the resulting site chronologies. Splitting the series at the release years, as Blasing et al. (1983) suggested, would create a missing period of several years in each series, which would leave little chances for an inter-chronology comparison and climate response analysis. Residual chronologies were used in the study since our purpose was mainly dendroclimatic (Cook et al., 1990). Desplanque et al. (1998) showed that this stan- dardization method extracted relevant climatic information from A. alba ring-width series in Alps forests similar to ours. A spline length was needed which would be flexible enough to filter the series growth releases but still able to produce residual series with climatic information. To solve this problem, we took the maximum signal-to-noise ratio (SNR) criterion to the chronology level. SNR is a measure of the common variance in a chronology scaled by a measure of the total variance of the chronology (Wigley et al., 1984; Cook et al., 1990). We compared our 10 chronologies and quantified their common signal by means of the inter-chronology SNR for spline lengths ranging from 5 to 50 years (Figure 2, left). The maximum SNR for the residual chronologies was obtained for spline lengths of 15 years (Figure 2). However, declines in the spline stiffness lead to a decrease in the 1st order autocorrelation coefficient of the resulting standard series (Figure 2, right). SNR between residual chronologies started to decrease for splines shorter than 15 yrs. In the case of the trees and conditions we are dealing with, residual chronolo- gies resulting from 15-yr spline standardizations constituted a trade-off between an efficient removal of the disturbance signal and problems related with adjusting too flexible curves to the growth-series (i.e. artificially creating negatively auto- correlated series (blue noise) by removing low frequencies). Thus, all subsequent analyses were performed using the 15-yr spline residual chronologies, and all the results presented were derived from them. We assume that such a spline fit may remove much long- and mid-term variation from the series, so it must be noted that our results are only referred to the short-term variation patterns of tree growth.
  7. 7. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 295 12 0.8 Inter-chronology S N R 10 0.6 0.4 AC Coefficient 8 0.2 6 0 4 -0.2 0 5 10 15 20 25 30 35 40 45 50 55 2 -0.4 0 -0.6 0 10 20 30 40 50 60 -0.8 Spline lenght (yr) L: Spline lenght(yr) Figure 2. Left: Mean inter-chronology Signal-to-noise-ratio (SNR) vs. spline length in years (com- parisons between residual (squares) & standard chronologies (triangles)). Right: first-order autocor- relation coefficients (AC) versus spline length (the thick line is the mean of the 10 chronologies). Despite SNR between standard chronologies increased at lower values of spline stiffness (as low as 5 years) (Figure 2), such flexible splines would imply a clear loss of climatic signal. This was evidenced by an accelerated variance decrease in all chronologies for spline lengths shorter than 10 years. Descriptive statistics were calculated for each chronology to allow comparisons among sites and with other dendroclimatic data sets (Fritts, 1976; Briffa and Jones, 1990). These statistics were: first order autocorrelation (r1), the percentage of vari- ation explained by the first principal component (VARpc1), signal-to-noise ratio (SNR), as well as standard deviation (SD) and mean sensitivity (MS). A time series of year-to-year sensitivity indexes (St ) was also calculated for each chronology, based on the formula St = | (It+1 − It ) * 2 / (It+1 + It ) |, for It = residual index value for the year t (Fritts, 1976), to analyse the variation of each chronology. We used the Expressed Population Signal (EPS) to establish a criterion to select a com- mon period where all chronologies would be reliable enough (Wigley et al., 1984). The common period 1902–1999 was selected based on a minimum EPS value of 0.85, which is a widely used threshold in dendroclimatic studies. 2.2. SPATIO-TEMPORAL VARIABILITY IN RADIAL GROWTH Shared growth variability can be interpreted as a common response to regional climatic signals (Tardif et al., 2003; Macias et al., 2004), and its changes along the 20th century as a signal of climatic changes which have affected the growth of silver fir. Moreover, the spatial distribution of these relationships and their temporal changes can also give information about homogeneous climatic areas or about where climatic gradients are more important for tree growth (Villalba et al., 1997). First, we performed a Principal Component Analyses (PCA) based on the cor- relation matrix for the common period 1902–1999 to evaluate the shared variance among residual chronologies. Pearson correlation and PCA were also computed for
  8. 8. 296 M. MACIAS ET AL. successive 20-yr periods with a 5-yr lag in order to evaluate the temporal changes of this shared variability. We also assessed the spatial variability of radial growth through the relationship between distance and correlation for pairs of chronologies for the study period, as well as its changes along the 20th century. This is a good way to analyze spatiotemporal relationships within a network of chronologies and the existence (or not) of spatial gradients in Abies alba radial growth (Fritts, 1991). 2.3. RADIAL GROWTH RESPONSE TO CLIMATE In mountainous regions such as the study area, temperature series have shown stronger inter-site relationships between distant sites than precipitation data, which are more variable locally (Agust´-Panareda et al., 2000). Seven sub-regional ı monthly average temperature and precipitation data series were obtained from 27 meteorological stations in the area (Figure 1, Table II). Meteorological stations were grouped according to their homogeneity based on the Mann-Kendall test us- ing HOM routine from the Dendrochronology Program Library (DPL; Holmes, 1996). Sub-regional datasets were then produced using the MET routine from the same software. These calculations are based on the average and standard deviation of each month for each station. In addition, Main Range and Peripheral Ranges se- ries of temperature and precipitation were constructed by combining the different sub-regional data sets. Five sub-regional datasets allowed a response function analysis for the period 1941–1994. Two of them, Montseny and Ripoll` s, started in 1951. Correlation and e response function analyses were performed for such periods using the program Den- droclim2002 (Biondi and Waikul, 2003) to quantify the climate-growth relation- ships between the different sets of regional climate series (monthly mean temper- ature, monthly total precipitation) and the residual radial-growth chronologies. In order to avoid the problem of multi-collinearity, commonly found in multi-variable sets of meteorological data, Fritts (1976) introduced a stepwise multiple regression on principal components to assess climate-growth relationships (response func- tion). The significance and stability of the calculated regression coefficients were estimated based on 1000 bootstrap estimates obtained by random extraction with replacement from the initial data set (Guiot, 1991). Climate-growth relationships were analyzed from the previous August up to September of the growth year. In these analyses, we used the sub-regional dataset corresponding to the area where each chronology was located. We used evolutionary response functions to analyse how the growth-climate rela- tionships changed through time and to detect these changes in the climatic response of Abies alba. First, a regional chronology was created by performing Principal Component Analyses on the chronology network. Two sub-regional chronologies were also built with the same procedure: one for the Main Range group (sites 1, 4, 5 and 6 as in Figure 1) and one for the Peripheral Ranges group (sites 2, 3, 7, 8
  9. 9. TABLE II Sub-regional temperature (T) and precipitation (P) studied datasets. Temperature Precipitation Sub Regional Temperature Precipitation ◦ Climate Mean C Annual sum (mm) m Series Total period 1941–1994 Total period 1941–1994 Station Latitude Longitude a.s.I. First Last First Last ◦ ◦ Candanch´ u 42 47 19 − 00 32 14 1613 1951 1975 1951 1975 SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH Jacetania/ 1910–1999 7.4 1910–1999 1655 Canfranc 42◦ 44 03 − 00◦ 31 24 1075 1910 1999 1910 1999 G´ llego a Sallent de G´ llego a 42◦ 46 26 − 00◦ 19 49 1285 1953 1994 1960 1999 Jaca 42◦ 34 05 − 00◦ 34 05 800 1943 1985 1930 1972 Jacetania/ 1941–1999 10.3 1941–1999 873 Sabi˜ anigo n´ 42◦ 31 08 − 00◦ 21 38 790 1941 1995 1941 1999 Hoya Nocito 42◦ 19 24 − 00◦ 15 21 931 1973 2000 – – Bolta˜ a n 42◦ 26 45 + 00◦ 04 00 643 – – 1951 1999 Cabdella 42◦ 27 55 + 00◦ 59 28 1270 1954 1992 1954 1994 Estany Gento 42◦ 30 28 + 01◦ 00 03 2120 1930 1985 1925 1985 Pallars 1940–1994 6.9 1926–1999 984 Estany de St. Maurici 42◦ 34 50 + 01◦ 00 17 1920 1953 2000 – – Espot 42◦ 34 28 + 01◦ 05 21 1310 1953 1991 1953 1991 ` Esterri d’ Aneu 42◦ 37 27 + 01◦ 07 31 940 – – 1955 2000 Tavascan 42◦ 38 15 + 01◦ 15 07 1100 1967 1994 – – Oliana 42◦ 05 00 + 01◦ 18 10 480 1931 1997 – – Alt Urgell 1940–2000 11.5 1940–1996 660 Organy`a 42◦ 12 43 + 01◦ 19 46 540 1972 1999 1915 1999 Adrall 42◦ 19 25 + 01◦ 23 39 642 1940 1996 1933 1996 Port´ Pimorent e 42◦ 33 + 01◦ 50 1600 – – 1966 1987 Cerdanya 1944–1996 7.9 1940–1994 902 La Molina 42◦ 20 02 + 01◦ 56 15 1704 1929 1998 1927 1998 Puigcerd` a 42◦ 26 07 + 01◦ 56 16 1145 1911 2000 1912 1974 Mean values are shown for the common period used in the study (1941–1994). Positive longitude: Eastern Hemisphere; negative longitude: Western Hemisphere. (Continued on next page) 297
  10. 10. 298 TABLE II (Continued) Temperature Precipitation Sub Regional Temperature Precipitation ◦ Climate Mean C Annual sum (mm) m Series Total period 1941–1994 Total period 1941–1994 Station Latitude Longitude a.s.I. First Last First Last ◦ N´ ria u 42 23 38 + 02◦ 09 20 1967 1950 1996 1934 1996 Ripoll` s e 1951–1996 9.1 1951–1994 983 Ribes de Freser 42◦ 17 60 + 02◦ 10 00 912 1930 1994 1942 1988 Ripoll 42◦ 12 01 + 02◦ 11 23 690 1975 2000 – – Vallter-2000 42◦ 26 06 + 02◦ 15 58 2180 – – 1961 1995 M. MACIAS ET AL. Cardedeu 41◦ 38 11 + 02◦ 21 35 195 – – 1951 1997 Montseny 1911–1996 11.8 1951–1996 894 Tur´ de I’Home o 41◦ 46 33 + 02◦ 26 03 1708 1961 1997 1932 1997 Girona 2 41◦ 58 20 + 02◦ 48 20 90 1975 1994 – – Girona 1 41◦ 58 36 + 02◦ 49 30 94 1911 1977 – –
  11. 11. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 299 and 10 as in Figure 1). Second, we built a long climatic series (1910–1999) using monthly meteorological data from Pic du Midi (B¨ cher and Dessens, 1991), and u Canfranc stations (see Figure 1 for location), which characterize well the climatic variability in the Central Pyrenees during the 20th century (Tardif et al., 2003). We performed evolutionary response functions based on the three different modes available in Dendroclim 2002 (Biondi, 1997, 2000): (1) moving response intervals, considering a 60-year fixed interval, and increasing the initial and final years of the analyses by one for each iteration; (2) forward evolutionary intervals, using the same initial year of the interval but increasing by one for each iteration the final year so that the intervals go progressively forward in time; (3) backward evolutionary intervals, using a fixed final year of the interval, and decreasing by one for each iteration the initial year, so that the intervals go progressively backward in time. 3. Results 3.1. CHRONOLOGY STATISTICS Statistical characteristics of the chronologies are shown in Table III. For the study period 1902–1999, EPS values were > 0.85 in all chronologies but in three: Pe˜ a n Oroel, Guara and Boavi showed values > 0.80 for this period. Their quality is still very good and they were kept. First-order autocorrelation was very close to zero for most of the standard chronologies due to the very flexible spline (15 years) used in the standardization (Figure 2). Mean sensitivity (MS) values were generally higher for chronologies located in the Peripheral Ranges (e.g., 0.18 for Moixer´ , 0.15 for o Oroel, 0.14 for Guara) and lower for chronologies located in the Main Range (e.g., 0.08 for Conangles, 0.11 for Boavi). Although this trend was observed, it was not possible to separate two groups according to their MS values. However, time series of Sensitivity (St ) gave more information (discussed later). 3.2. SPATIAL VARIABILITY IN RADIAL GROWTH The average Pearson correlation coefficient for the network of chronologies and for the period 1902–1999 was 0.44 ± 0.09 (mean ± SD). All correlation pairs were positive and highly significant ( p < 0.01). The lowest correlation values were found when comparing sites located in the Main Range against sites located in the Peripheral Southern Ranges (sites 5 vs. 10 = 0.20; 4 vs. 8 = 0.28; 1 vs. 2 = 0.35; see Figure 1 for name and location). Low correlation coefficients were also found between sites located at the western Peripheral Southern Ranges (wPSR) and sites located at the eastern Peripheral Southern Ranges (ePSR) (sites 2 vs. 10 = 0.32; 2 vs. 8 = 0.31; 3 vs. 8 = 0.35). Highest values were achieved for pairs of chronologies within the wPSR (sites 2 vs. 3 = 0.61) or within ePSR (sites 7 vs. 8 = 0.61), as
  12. 12. 300 TABLE III Chronology characteristics: expressed population signal (EPS), n◦ of cores, mean radial growth, mean sensitivity (MS), standard deviation (SD), first-order autocorrelation (r1), signal-to-noise ratio (SNR) and variance explained by the first principal component (VARpc1). The common period was set as 1902–1991 Radial Common interval: growth Standard chronology Residual chronology 1902–1999 detrended series EPS > 0.85 N◦ of mean (SD) Study site since cores (mm) MS SD r1 MS SD SNR VARpc1 1 Aztaparreta 1844 28 1.83(0.17) 0.13 0.11 −0.01 0.13 0.11 8.53 42.51% 2 Pe˜ a Oroel n 1905∗ 28 2.67(1.13) 0.14 0.13 −0.04 0.15 0.13 3.10 64.60% 3 Guara 1905∗ 30 2.99(1.20) 0.16 0.13 −0.13 0.13 0.12 5.95 77.68% 4 Conangles 1828 30 0.92(0.44) 0.11 0.09 0.07 0.09 0.08 7.00 45.87% M. MACIAS ET AL. 5 La Mata de Val` ncia e 1790 35 0.89(0.43) 0.13 0.11 −0.20 0.12 0.10 17.74 60.40% 6 Boavi 1903∗ 30 2.99(1.22) 0.11 0.09 0.02 0.11 0.09 5.68 52.02% 7 Boumort 1892 40 1.58(0.82) 0.14 0.14 0.08 0.14 0.13 6.55 40.08% 8 Moixer´o 1869 31 1.85(1.13) 0.16 0.14 −0.03 0.17 0.14 12.84 51.46% 9 Setcases 1886 31 2.05(1.14) 0.14 0.14 0.13 0.14 0.14 5.59 35.88% 10 Montseny 1871 30 1.23(0.68) 0.18 0.19 0.24 0.15 0.13 8.23 49.31% ∗ n EPS > 0.8 since: Pe˜ a Oroel, 1902; Guara, 1900 and Boavi, 1897.
  13. 13. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 301 well as for a pair within the Main Range (sites 1 vs. 9 = 0.63). Despite the spatial coherence of these extreme values, a look at the bulk of correlation pairs did not show a clear zonation. The variance explained by the first principal component (PC) of the PCA for all residual chronologies was 49.43 %. The first PC had positive loadings for all chronologies, and it was interpreted as the common variability of the network of chronologies, that is, as a macroclimatic signal. Thus, the time series of the first PC was used as a regional chronology. The second, third and fourth PCs explained a cumulative variance of 25.2% and represented sub-regional to local sources of variability. 3.3. TEMPORAL VARIABILITY IN RADIAL GROWTH As a result of the management history of the studied stands, we found a high frequency of releases when looking at the non-standardized raw growth data, most probably due to logging, in the 1910s, 1920s and 1930s, which was higher in the Peripheral-Ranges than in the Main-Range groups of chronologies (Figure 3). A 20 Peripheral R. Frequency of growth-changes > 0.75 (%) Main R. 15 10 5 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 Time (years) Figure 3. Inferred logging history in the studied sites based on the yearly relative frequency of radial- growth changes greater than 75%. Results are presented separately for the Peripheral-Ranges and Main-Range sites.
  14. 14. 302 M. MACIAS ET AL. 70 0.7 P e ars o n C o rrelation C o efficient 60 0.6 %Variance1st PC 50 0.5 40 0.4 y = 0.0132x + 0.3323 30 2 0.3 R = 0.6812 y = 1.1224x + 41.429 20 Fmodel=32.0477, p<0.0001 0.2 R2 = 0.6648 Slope: t=5.66, p<0.0001 Fmodel=29.745,p<0.0001 10 0.1 Slope:t=5.45,p<0.0001 0 0 1902-1920 1911-1930 0 0 1921-1940 1941-1960 1951-1970 1971-1990 1981-1999 1931-195 1961-198 Figure 4. Pearson correlation coefficients for the A. alba chronology network (20-yr periods with a 5-yr lag, squares) and percentage of variance expressed by the 1st component of the PCA (20-yr periods with a 5-yr lag, triangles). Linear regressions were applied in both cases. Continuous line box contains statistics from the linear regression for the % of Variance of the 1st PC. Dashed line box contains statistics from the linear regression for the Pearson Coefficients. Both cases showed highly significant ( p < 0.0001) models (F test) and slopes (t test). high frequency of positive growth changes greater than 75% was also observed in the 1950s, this time being higher in the Main-Range sites. Since the 1960s the frequency of releases has greatly decreased, except for a slight increase in the late 1980s. Both average Pearson correlation and variance explained by the first PC of the ten residual chronologies showed a significant increase in the common variability of chronologies along the 20th century (Figure 4). Correlation values and variance explained by the first PC rose from r = 0.31 and 38.8 % in the beginning of the 20th century to r = 0.58 and 62.6 % in the end of the analyzed period. The cumulative variance of the second, third and fourth PCs showed a steady and significant decline during the 20th century from 38.7% to 26.3%, which implied a decrease in sub- regional to local variability (not shown). Both the linear regressions and their slopes were highly significant ( p < 0.0001). Thus, the common macroclimatic signal (common variability) has been increasing markedly during all the studied period. The relationships between inter-site correlation and distance have also steadily increased during the 20th century (Figure 5). During the first half of the 20th century (1902–1950), correlation between pairs of chronologies decreased significantly ( p < 0.05) with increasing distance between sites, i.e., there was a spatial gradient in the growth of A. alba in the Pyrenees. However, the gradient disappeared during the second half of the century (1951–1999), when distant chronologies showed more similar growth patterns than before. Thus, for the first half of the 20th century, local or at least sub-regional features had a stronger effect on silver fir growth in the Pyre- nees than for the second half, when the signal of regional factors became dominant.
  15. 15. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 303 0.8 y = 3E-05x + 0.4996 R2 = 0.0003 0.7 Fmodel=0.0128, p>0.1 Slope: t=0.11, p>0.1 0.6 Pearson Coefficient 0.5 0.4 0.3 0.2 y = -0.0005x + 0.4373 R2 = 0.106 0.1 Fmodel=5.0969, p<0.05 Slope: t=-2.26, p<0.05 0 0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 Distance (km) Figure 5. Correlation coefficients vs distance between chronologies. The relationship was negative and significant for the period 1902–1950 (squares), while it disappeared for the period 1951–1999 (triangles). Note also the higher correlation values for the second half 20th century. Some series of year-to-year sensitivity (St ) showed great variability, whereas others were more regular throughout the 20th century (Figure 6). Two groups were formed according to the variances of St of each chronology (t-test, p < 0.005), which were different by one order of magnitude: a high variance of St Group: sites 2, 3, 7, 8, 9 and 10 (see Figure 1 and Table I for names and location), and a low variance of St Group: sites 1, 4, 5 and 6. Note that all chronologies of the low variance group are located in the Main Range, whereas all chronologies of the high variance group but one (Setcases, site 9) are located in the Peripheral Ranges. Although being located in the Main Range, Setcases has the particularity of being the closest site to the Mediterranean Sea, only ca. 65 km from it, and so a higher Mediterranean influence in terms of precipitation regime is expected for this site than for the other Main Range sites. We found peaks in St around 1930s, 1960s and 1980s, which reached much higher values in the high-variance than in the low-variance group. Sensitivity increased during the 20th century ( p < 0.05), but the increase was stronger in the high-variance group (Figure 6). During the second half of the 20th century, there was a strong and highly significant ( p < 0.0001) increase in St in the high-variance group, whereas no significant increase could be detected in the low-variance one. 3.4. RADIAL GROWTH-CLIMATE RELATIONSHIPS All sub-regional datasets showed a Mediterranean influence characterized by pre- cipitation maxima in spring and autumn, and a relative minimum in summer (Figure 1). Annual precipitation was highest in the westernmost site (Jacetania-
  16. 16. 304 M. MACIAS ET AL. High Variance Group 0.6 y = 0.0004x - 0.6775 y = 0.0021x - 3.995 0.5 2 R = 0.0808 R2 = 0.3725 Fmodel=7.644, p=0.007 . , Fmodel=26.114, p<0.0001 el=26.114, 0001 Slope t=2.76, p=0.007 t= , Slope: t=5.11, p<0.0001 e: 0. 1 0.4 0.3 0.2 Year-to-year sensitivity( St) 0.1 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Low V a riance G r oup 0.6 y = 0.0002x - 0.3729 y = 0.0005x - 0.8849 2 0.5 R = 0.0807 R 2 = 0. 0.0564 Fmodel=7.635, p=0.007 odel=7.635, Fmodel=2.628, p>0.1 = .6 0.4 Slope: t=2.76, p=0.007 e: Slope: t=1.62, p>0.1 0.3 0.2 0.1 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Figure 6. Year-to-year sensitivity time series (St ) for the two groups of chronologies: high variance of St (up), low variance of St (down). The thick line is a 10-yr centred running mean. The dashed straight line is a linear regression for the period 1902–1999, whereas the continuous straight line is a linear regression for the sub-period 1950–1999. Note that the maximum values observed in the high-variance group were much higher than those reached by the low-variance group. G´ llego: Table II, Figure 1), where climate is under greater oceanic influence, and a lowest in the stations far from the sea (Alt Urgell). We noted a general process of aridification in all datasets during the 20th century. Taking the period 1941–1994 as the study period, annual mean temperature has been rising, especially since the 1970s (Figure 7.A, B). The temperature increase was observed both in winter (De- cember to March) and summer (July, August) months. The temperature increase in winter was more pronounced in the Western and Central Main-Range sub-regions,
  17. 17. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 305 Figure 7. (A). Recent trends (slope of the linear regression; b in y = a + bx) of monthly mean temperature and monthly total precipitation for the seven sub-regional datasets and Pic du Midi climate data (abbreviations follow Figure 1). Squares correspond to p < 0.05. The period analysed is 1941–1994, except for Montseny and Ripoll` s (1952–1994) (B). Two examples of the recent climatic e trends starting in the 1980s (aridification) corresponding to the average departures for the seven sub- regional datasets (see Figure 1) in mean July temperature and total August precipitation from the 1941–1994 averages. whereas the summer warming was more intense in the Central Pyrenees sub-regions. In some cases, precipitation sums have also been declining, especially in summer (July, August) and March. Positive trends in precipitation were observed in October everywhere and their value decreased eastwards. The two easternmost sub-regions (Ripoll` s and Montseny) did not show these trends, but a process of temperature e increase was evident since the 1970s. Generally, the temperature rise and the precip- itation decrease in February, March, July and August have been more pronounced since the 1980s (Figure 7.B)..
  18. 18. 306 M. MACIAS ET AL. Table IV summarizes the general climate response for all sites based on the relationships between A. alba regional chronology (the first Principal Component of the chronology network) and the regional climatic series for all the study area, and also separately for the Main Range and the Peripheral Ranges. Variance explained by the first PC was 58.6% and 52.8%, for the Main-Range and Peripheral-Ranges groups, respectively. As mentioned in methods, these PCs were used as the Main- Range and Peripheral-Ranges sub-regional chronologies. All chronologies showed significant responses to the climate conditions of the late summer prior to the growth year, especially a negative and generalized re- sponse to September temperature (Table IV). During the year previous to tree-ring formation, tree-growth showed significant (p < 0.05) positive relationships with August precipitation for the Main Range chronology, and, to a lesser degree, with September precipitation for the Peripheral Ranges chronology. During the growth year, radial growth was positively related to February temperature and to July pre- cipitation (restricted to the Peripheral Ranges Chronologies). Response functions performed for each individual site with the climatic data of the closest sub-region climate series showed similar results, with the only difference being a more ex- tended negative response to previous late summer temperature by the Main Range Chronologies (August to October) (not shown). 3.5. TEMPORAL INSTABILITY OF GROWTH-CLIMATE RELATIONSHIPS We analysed the moving-interval response functions based on 60-yr intervals for the regional chronology, and for the Main-Range and Peripheral-Ranges sub-regional chronologies (Figure 8). The negative response to previous August temperature has extended until October, and a positive response to previous August precipitation has shown to be important, extending into September during the 1980s. Both changes suggest a longer water-stress season during the year prior to growth at the end of the 20th century than at the beginning of the past century. When performing the sub-regional analyses, we found slightly different results for the Main Range and the Peripheral Ranges chronologies. From 1986 to 1999, negative responses to previous October temperature appeared in the Main Range, whereas positive responses to previous August precipitation became stronger at the end of the 20th century. Positive responses to previous summer precipitation extended from August to September in the Peripheral Ranges from 1986 to 1993; for the same period, negative responses to previous September temperatures were also found in the Peripheral Ranges. During the year of tree-ring formation, the negative effect of August precipitation in the Main Range was not significant since 1982. The positive effect of current July precipitation in the Peripheral Ranges weakened and June precipitation became more important for tree growth at the end of the 20th century. The analyses based on forward and backward evolutionary intervals confirmed these findings (results not presented).
  19. 19. TABLE IV Correlation (C) and response (R) functions based on bootstrap correlation and orthogonal regression on residual chronologies and monthly climate data from previous September to current August (months abbreviated by capital letters correspond to the year of growth). Results correspond to the regional (up), the Main-Range (middle) and the Peripheral-Ranges chronologies (down). Only significant values are presented ( p < 0.05) Year t − 1 Year t SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH Chronology Variable Analysis a s o n d J F M A J J A S Regional T C −0.52 0.37 R −0.23 P C 0.31 0.29 0.21 R Main range T C −0.35 −0.49 0.35 R −0.21 P C 0.31 0.29 R 0.23 Peripheral range C −049 0.34 T R −0.26 C 0.28 0.31 P R The analyses are based on regional climatic series (T, mean monthly temperature; P, total monthly precipitation) for all the study area, the Main Range and the Preipheral Ranges covering the 1941–1994 period. The window starts with August of previous year (t − 1) and ends with September of the year of growth (year t, months abbreviated by capital letters). Only 307 bootstrap correlation (C) and response (R) significant values are displayed ( p < 0.05).
  20. 20. 308 M. MACIAS ET AL. A Regional chronology S A T P year t J J Time (months) M A M F J year t-1 d n o s a 1970 1975 1980 1985 1990 1995 1970 1975 1980 1985 1990 1995 2000 Time (60-year intervals) B Main Range Peripheral Ranges S A T P T P year t J J M A M F J d year t-1 n o s a 197019751980198519901995 197019751980198519901995 197019751980198519901995 1970197519801985199019952000 Time (60-year intervals) Figure 8. Moving-interval response functions showing the significant coefficients ( p < 0.05) based on the relationships between mean monthly temperature (T) or total monthly precipitation (P) and the Regional (up), Main-Range (down and left) and Peripheral-Ranges (down and right) chronologies. Months abbreviated by capital letters correspond to the year t of growth, and the rest of months correspond to the previous year t − 1. The displayed years in the abscissa axis correspond to the last year of 60-yr moving intervals. The symbol type indicates the type of relationship: circles, negative coefficients; squares, positive coefficients. The symbol colour indicates the strength of the relationship: grey symbols, coefficient = 0.1–0.2; black symbols, coefficient = 0.2–0.3.
  21. 21. SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 309 4. Discussion Standardizing the series with a flexible spline produced reliable chronologies since A. alba climatic signal was successfully extracted, as seen in the response functions, which show similar patterns and magnitudes to those found in the Alps for the same species (Rolland, 1993; Desplanque et al., 1998; Rolland et al., 1999). The spatial synchrony between sites was low in 1902–1950 (r < 0.4 at distances > 100 km) but high in 1951–1999 (r > 0.5 at distances > 200 km), which partially agrees with results presented by Rolland (2002). These findings emphasize the greater local variability in radial growth of low-elevation species from mesic sites such as A. alba, which caused the low spatial synchrony between sites in the first half of the 20th century. However, during the second half of the last century, A. alba showed a greater similarity in radial growth at long distances, which suggests that a regional climatic factor was modulating radial growth (Tardif et al., 2003). This last behaviour was similar to that usually observed for conifers from high-elevation and harsh sites such as P. uncinata, which usually show greater inter-site similarity in radial growth at long distances (>500 km). Spatial synchrony between distant chronologies is a valuable property for dendroclimatic reconstructions, but our study has demonstrated that it may not be ascribed only to species from harsh sites. The studied area has shown a trend towards an aridification during the 20th century, especially since the 1970s and 1980s (Figure 7). In NE Iberian Peninsula, a greater drought stress might have been induced by the recent warming detected in the Pyrenees starting in the 1980s, which caused severe summer droughts in the 1980s and 1990s (B¨ cher and Dessens, 1991; Tardif et al., 2003; Brunet et al., u 2005). Southern-Pyrenean areas close to the Mediterranean Sea did not show a decrease in precipitation (Pi˜ ol et al., 1998). n Silver fir response to summer drought seems to be general as inferred from the presented response functions (Table IV). Drought during the summer prior to growth was the most limiting factor for radial growth, which agrees with the “drought-avoidance” strategy of the species (Rolland et al., 1999; Aussenac, 2002). A. alba has lower water-use efficiency than other fir species from more xeric areas (Guehl and Aussenac, 1987; Guehl et al., 1991). Thus, it is expected that A. alba would be a species highly sensitive to drought. Increasing water stress during the second half of the 20th century might be the cause of the higher synchrony in tree growth (Figure 4) and the increase in year-to- year sensitivity (Figure 6). An elongation of the period of water stress might explain why silver-fir growth shifted in the mid 1980s from being sensitive to August temperature to being sensitive up to September and even October temperature (Figure 8). Other authors noted similar growth responses in the Pyrenees in other subalpine conifers (P. uncinata) during this decade (Guti´ rrez et al., 1998; Tardif e et al., 2003). The stands in the Peripheral Ranges have experienced these changes with special intensity, as seen in the higher increase of sensitivity when compared with the
  22. 22. 310 M. MACIAS ET AL. Main Range chronologies, which showed an increase in the length of previous late summer temperature response. Peripheral chronologies showed this response and also a shift in the response to precipitation from previous August to previous September in the second half of the 20th century (Figure 8). Besides, they showed positive relationships with current early summer precipitation, which changed from July at the beginning of the study period to June at its end, also suggesting an elongation of the water stress period (Figure 8). These results are logical since these peripheral forests grow under the most stressful conditions (drier climatology). Main Range sites are usually located at the bottom of upper valleys or on N- NW slopes, at the base of high mountains (2500 to 3000 m). Snowmelt during late spring and summer could guarantee water supply in the soils of these sites, as they do not show any positive response to current late spring-summer precipitation. However, Peripheral Ranges are lower (usually less than 2000 m) and dryer, with silver fir forests located in northern slopes not far away from the summit. In this case, there is probably not much water supply coming from snowmelt and Peripheral Chronologies show a current late spring-summer positive response to precipitation. The positive influence of February temperature on growth (Table IV) is probably comparable to the positive response to current March temperature in the French Alps for the same species (Rolland et al., 1999). Warmer Februaries might accelerate snowmelt (or at least stop snow accumulation), favouring soil warming and thus enhancing an earlier start of the growing season. However, the effect of cold winters on A. alba growth over the area might not be strong and general given the fact that such relationship did not show to be significant when performing the bootstrap response functions and only showed significance in the less restrictive correlation functions. Silver-fir populations have been receding in the Iberian Peninsula after their post-glacial expansion (Huntley and Birks, 1983) due to climate, but also because of competition with Fagus sylvatica in the last four millennia (Blanco et al., 1997) and intense logging in the last centuries (Jalut, 1988). Nowadays, A. alba reaches its SW distribution limit in the Pyrenees, where previous summer drought is a major limiting factor for silver fir growth. Due to the ecological requirements of silver fir, most of the Iberian Peninsula under Mediterranean influence constitutes a ‘desert’ to the species, which only finds ‘oasis’ in very special places such as northward slopes and valley bottoms where slope aspect, moist climate and deep soils allow it to strive. These places are mainly located in the Main Range, where most of A. alba forests are found. The scattered small stands in the southern Peripheral Ranges form relict populations of former glacial refuges whose future growth performance is uncertain under current warming trends in the light of our results. The presented climatic signal extracted from Abies alba growth series makes this species a reliable monitor of the effects of climate change on forests in the Pyrenees. More efforts should be put to improve the present network of chronologies, especially in the central Pyrenees, and to relate their growth patterns to current climatic patterns including synoptic situations, with the aim of assessing their potential association
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