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Climate and historical stand dynamics in the tropical pine forests of northern Thailand
Forest Ecology and Management 257 (2009) 190–198
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Climate and historical stand dynamics in the tropical pine forests of northern Thailand Heidi Zimmer *, Patrick Baker School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 May 2008 Received in revised form 23 August 2008 Accepted 25 August 2008
Forest recruitment is the outcome of local- and regional-scale factors such as disturbances and climate. The relative importance of local- and regional-scale factors will determine the spatial scale at which temporal pulses of recruitment occur. In seasonal tropical forests, where the annual dry-season is a critical bottleneck to seedling survival, multi-year periods of relatively cool, wet dry seasons may be required for successful tree recruitment. Consequently, when such conditions are present, region-wide synchronisation of recruitment may occur. To examine the case for regional synchronisation of forest dynamics in the seasonal tropical pine forests of northern Thailand, we investigated forest age structures at three spatial scales: stand, site and region. We compared forest age structures with instrumental climatic records beginning in 1902. We found significant statistical evidence of synchronous recruitment at the stand- and site-scales, but not at the regional-scale. While correlations between recruitment and climate were not statistically significant, recruitment success was often linked to favourable climatic conditions. For example, recruitment at all sites was associated with multi-year periods of cool-wet dry seasons. The lack of significant correlations between recruitment and climate appears to reflect complex interactions among local disturbance history, regional climate variability and pine recruitment. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Dendrochronology Pinus kesiya Pinus merkusii Regional synchrony Tropical forest recruitment
1. Introduction The successful recruitment of new trees in a forest is a major determinant of long-term forest dynamics and the distribution of local and regional forest types. In any forest, recruitment is influenced by processes that occur across a range of spatial and temporal scales (Oliver and Larson, 1996). At regional and global scales, the roles of climate and geology on plant distributions are well-documented (Prentice et al., 1992; Franklin, 1995). Within individual stands and across landscapes, disturbances and biotic interactions are important influences on regeneration success, forest dynamics, and the relative local abundance of tree species (Botkin et al., 1972; Pacala et al., 1993, 1996). Interactions across scales may also be important in determining forest dynamics. Local-scale processes, such as disturbances, may be strongly influenced by processes at larger scales, such as climate. For example, the occurrence of landscape-scale fires is directly influenced by regional climate dynamics (e.g., Clark,
* Corresponding author. Present address: Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, PO Box 137, Heidelberg, VIC 3084, Australia. Tel.: +61 3 9450 8648; fax: +61 3 9450 8799. E-mail address: [email protected] (H. Zimmer). 0378-1127/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2008.08.027
1989; Westerling et al., 2006). Regional-scale, supra-annual climate events, such as ENSO-induced droughts, may also influence seedling recruitment by making the environmental conditions for germination and establishment harsher than usual. For tree species in which recruitment is disturbance-dependent, successful recruitment may require the synchronous occurrence of a disturbance and the appropriate environmental conditions. In seasonal tropical forests, the dry-season drought is a significant source of mortality for seedlings of many tree species (Troup, 1921; Engelbrecht and Kursar, 2003; Pearson et al., 2003). High temperatures and low relative humidity generate high vapour pressure deficits, which lead to severe water stress in seedlings, particularly small seedlings that do not have wellestablished root systems (Engelbrecht et al., 2006; Slot and Poorter, 2007). In addition, these same microclimatic conditions make seasonal tropical forests prone to fire. While fires in seasonal tropical forests are often low-intensity surface fires (Laurance, 2003; Barlow and Peres, 2004), mortality among seedlings is typically high (Baker et al., 2008). Consequently, successful recruitment of trees in seasonal tropical environments may depend on the occurrence of 3–4 years of mild (i.e. cool-wet) dry seasons (Troup, 1921; Bullock et al., 1995). Where regional climatic conditions are important in determining recruitment success and the occurrence of disturbances at the
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local-scale, regional synchronisation of forest dynamics may occur. While the relative importance of local and regional processes on forest dynamics is not well understood, several recent papers have provided evidence of regionally synchronised dynamics in temperate forests (Villalba and Veblen, 1997; Barton et al., 2001; North et al., 2005; Brown, 2006; Carrer et al., 2007). For example, Barton et al. (2001) demonstrated that large-scale climatic patterns in southwestern North America influence moisture availability and fire occurrence, which determine tree age structures, as well as growth and mortality rates. In Patagonia, Villalba and Veblen (1997) showed that seedling survival was higher during cool-wet periods and lower during warm-dry periods, leading to strong correlations between decadalscale climate variation over the past 40 years and forest age structure. In this paper we examine the case for regional synchronisation of forest dynamics in low-diversity tropical pine forests in continental Thailand. To do this, we used reconstructed pine recruitment histories to examine patterns of recruitment synchronisation at the stand-, site-, and regional-scales and compared them to local and regional instrumental records. The two pine species that are commonly found in these forests, Pinus kesiya Royle ex Gordon and Pinus merkusii Jungh et De Vriese, both form reliable annual growth rings, are sensitive to climate and have been the subject of recent dendroclimatic investigations in northern Thailand and Laos (Buckley et al., 1995, 2005, 2007a,b; Pumijumnong and Wanyaphet, 2006). We hypothesised that if climate was the primary limitation to recruitment, then recruitment should be synchronous on a regional-scale and associated with periods of relatively mild climatic conditions. In contrast, if recruitment is primarily a function of local disturbances then recruitment should be synchronous at the stand-scale, but asynchronous at site and regional-scales and not necessarily associated with mild climatic conditions. 2. Methods 2.1. Study area and species To determine the relative influence of local and regional processes in recruitment success of tropical pine forests in continental Southeast Asia, we established a spatially nested sampling strategy with replicated study plots at stand, site, and regional-scales. In total we assessed recruitment dynamics at 10 tropical pine forest stands at four sites within the northern and northeastern regions of Thailand (Fig. 1). The four study sites were Doi Chiang Dao Wildlife Sanctuary (two stands: DCD and MCD), Wat Chan (two stands: BWC and WCR), Thung Salaeng Luang National Park (three stands: TSL1-3) and Nam Nao National Park (three stands: NAM1-3). In northern Thailand, P. kesiya occurs at high elevations (1000– 1700 m) on exposed ridges with sandy soils (Gardner et al., 2000) and P. merkusii occurs at lower elevations, usually below 1000 m (Gardner et al., 2000) on poor, well-drained soils (Turakka et al., 1982). P. kesiya generally grows faster and has a shorter lifespan than P. merkusii. Both species produce cones annually (Cooling, 1968), but little is known of their recruitment dynamics. Koskela et al. (1995) reported that young P. merkusii grow in a grass stage characterised by thick stems and little growth in height, during which seedlings are less vulnerable to fire (Cooling, 1968). In contrast, P. kesiya seedlings start shoot growth immediately after germination and reach their height of fire resistance, 2.5 m, at 5 years. P. kesiya also has a shallower taproot and smaller seeds. These characteristics may make P. kesiya more susceptible to drought stress (Turakka et al., 1982).
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2.2. Sampling design At each of the four sites we identified 2–3 representative stands that were 1–10 km apart. We established 2–5 transects in each stand, with the starting point of the first transect chosen subjectively, depending on the size and heterogeneity of the stand. At randomly selected distances along the transect, we established 2–6 study plots (typically 20–60 m apart). We used the pointcentred quarters method (Cottam et al., 1953) to identify four sample trees in each plot, and to estimate stand density and basal area. We measured the distance from the plot centre to the nearest tree <10 cm diameter at breast height (DBH; 1.35 m) in each of the four quadrats. For each of these trees we measured DBH and recorded species identity. Two cores were extracted from each pine tree at breast height. If any core was judged to have missed the pith by >5 cm, it was discarded and another core was taken. We added plots to each transect until we had sampled a minimum number of 20 pines within each stand. 2.3. Chronology development and standardisation All cores were prepared following standard dendrochronological procedures (Stokes and Smiley, 1968). Cores taken from the same stand were visually crossdated to ensure accuracy in the allocation of annual growth rings to the year in which each was produced. This was particularly important for P. merkusii, which frequently produces false rings (Buckley et al., 1995; Fig. S1 in Supplementary Material). We analysed the preliminary visual crossdating using the program COFECHA (Holmes, 1983; GrissinoMayer, 2001), which calculates mean inter-series correlations between each individual ring-width series and a mean chronology. Mean chronologies were generated for each stand and site from all ring-width series at the respective sample level. Some individual ring-width series showed irregular growth patterns and/or unclear ring structure, and correlated poorly with the mean chronology. These series were re-checked and either corrected or removed from further analyses. The remaining set of ring-width series was then standardised to create a master chronology for each stand and site. We used the program ARSTAN (Cook and Holmes, 1985) to standardise the chronologies. Ring-width values were transformed into standard values with a mean of 1 and with stable variance using an adaptive power transformation (Druckenbrod, 2005). We did this to ensure that our ring-width series met the assumptions of normality and equal variance required for subsequent regression analyses with climate variables. The transformed series were de-trended using a negative exponential curve or a straight line to remove any age-related growth trend. The rejection rate of tree core samples due to dating problems was relatively low for tropical tree species. Of the 273 trees that we sampled, 231 (84%) were included in the final analyses of age structure and recruitment synchrony. 2.4. Relating age structures to climate Tree ages were derived from the crossdated tree-ring series. Where a core did not intersect the pith of a sample tree, the missing distance to the pith was estimated using Duncan’s geometric method (Duncan, 1989). This estimated distance was then divided by the average ring-width of the five rings closest to the pith, resulting in an estimate of the number of missing years between the last ring on the core and the missed pith. To account for the number of years required for the trees to grow to 1.35 m (i.e., coring height) we added 5 years to the pith ages obtained from the tree cores. Although the numbers of years
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Fig. 1. Locations of the four study sites in northern and northeastern Thailand.
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required to grow to breast height may vary, the only published studies (Turakka et al., 1982; Savage, 1994, Koskela et al., 1995) on the recruitment of these pines all suggest that seedlings require an average of 4–6 years to reach breast height. Age structure diagrams were created for each stand (n = 10), site (n = 4), and region (n = 2) (see Section 2.1). Establishment dates were grouped into 5-year periods (e.g., 1900–1904); for brevity we refer to trees establishing in a 5-year period by the first year of that period (e.g., 1900 represents 1900–1904). To determine if establishment was correlated with environmental conditions, we compared climate data to the age structure results at each spatial scale. To test the hypothesis that climate variations influence tree recruitment in these seasonal tropical forests (e.g., Troup, 1921), we compared dry-season (February–April, Fig. 2) climate data, expressed as 5-year running averages of the deviations from the long-term mean values, with the age structure diagrams. Climate data for each of the four sites consisted of monthly records for total rainfall, number of rainy days and minimum and maximum mean temperatures. Instrumental climate records for Chiang Dao and Wat Chan were taken from the Mae Hong Son meteorological station (198160 N, 978560 E), approximately 126 km and 37 km away from the sites, respectively. Instrumental climate records for Thung Salaeng Luang and Nam Nao were taken from the Petchabun meteorological station (168250 N, 1018080 E), approximately 30 km and 58 km away from the sites, respectively. Mean annual precipitation at Mae Hong Son was 1264 mm and at Petchabun was 1122 mm. From November to March both stations record a mean of <50 mm rainfall (Fig. 2). Mean temperature (January/July) at Mae Hong Son was 20.5/26.8 8C and at Petchabun was 24.3/27.7 8C. Instrumental climate data covered the periods 1911–2002 (Mae Hong Son precipitation) and 1951–2006 (Mae Hong Son and Petchabun temperatures and Petchabun precipita-
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tion). As a measure of water available to plants we used the Palmer Drought Severity Index (PDSI), which estimates drought severity using a water-balance equation (Alley, 1984). We used the selfcalibrating PDSI 0.58 0.58 gridded dataset derived from the algorithms developed in van der Schrier et al. (2005) for grid cells centred on each of the sites; 198250 N, 988750 E (Chiang Dao), 198250 N, 988250 E (Wat Chan), 168750 N, 1008750 E (Thung Salaeng Luang), and 168750 N, 1008250 E (Nam Nao). PDSI data for each site were available for the periods 1901–2002. To compare recruitment to climate data we used a general linear model (GLM) framework. Because recruitment data are based on counts and are bounded by zero, simple correlations or linear regressions of climate against recruitment would be inappropriate because the count data violate the distributional assumptions of these approaches. We used two different approaches within the GLM framework to account for this. First, we used the raw count data and specified a poisson error structure appropriate for such data for the GLM. However, because our data were overdispersed (i.e., residual deviance >degrees of freedom), we used a quasipoisson error structure in all of our analyses (Crawley, 2005). Second, we converted the recruitment data to a binary variable (i.e., presence/absence of recruitment) and used a binomial error structure for the GLM. In both cases we considered the models to be significant if the parameter estimates had a < 0.05. 2.5. Analysis of recruitment synchrony To determine if recruitment was synchronous at various spatial scales, we used a moving-blocks Monte Carlo simulation approach. We treated recruitment at each stand as a binary variable in which each year in each chronology was assigned a ‘1’ if recruitment had occurred or a ‘0’ if it had not. To compare the recruitment periods between two sites the values (either 0 or 1) for each year were multiplied together. Where synchronous recruitment occurs, a value of 1 is returned; where no recruitment occurs or recruitment only occurs in one site, a value of zero is returned. The number of occurrences of synchronous recruitment for a given period of years (i.e., block length) can be assumed in the absence of temporal autocorrelation to follow a hypergeometric distribution. To test for recruitment synchrony, we compared the frequency distribution of synchronous recruitment events that occurred in a pair of sites to 10,000 Monte Carlo simulations in which blocks of contiguous 20year periods are reordered randomly from a randomly selected start year. While the dataset included trees that established as early as 1741, we conducted our analyses for the periods 1800– 2005, as prior to 1800 only three trees recruited. We compared recruitment among the two to three stands within each site, among the four sites, and between the two regions, with the randomised data, to test the hypothesis that recruitment happened in non-random synchronous pulses. We conducted all analyses in the statistical program R, version 2.6.2 (R Development Core Team, 2005). 2.6. Recruitment and local factors
Fig. 2. Mean monthly rainfall and temperature recorded at (a) Mae Hong Son meteorological station (northern Thailand) and (b) Petchabun meteorological station (northeastern Thailand).
To assess the role of local disturbance on recruitment dynamics, we analysed growth releases from individual tree-ring records to provide a proxy measure of local disturbance occurrence. These were compared with the timing of recruitment periods and the climate records. Growth releases were identified using the percent growth change method (Nowacki and Abrams, 1997) implemented in the stand dynamics sub-routine of ARSTAN (Cook and Holmes, 1985). The individual chronologies were examined using a 10-year running average that compared average ring-widths for 10 years
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prior to, and 10 years after, a given year to ascertain whether a change in growth rate had occurred. Percent growth change was used to determine the size of the growth release: a 100% increase was considered to represent a major release and a 50–99% increase represented a moderate release (Lorimer and Frelich, 1989). 3. Results 3.1. Recruitment histories Among the 231 trees that we sampled in north and northeastern Thailand, ages ranged from 12 to 269 years old for P. kesiya and 12 to 234 years old for P. merkusii. Recruitment varied considerably within and among stands (Fig. 3). Most of our study stands had two or more age cohorts. In some stands (e.g., TSL1-3, NAM2) there were two distinct age cohorts. In other stands (e.g., both Wat Chan sites, NAM3, MCD) there was only one discrete age cohort, but other older or younger trees were scattered in low numbers across the age distribution. One of the Chiang Dao stands (DCD) was unique in having only a single age cohort. The two Chiang Dao stands, DCD and MCD, were the youngest in our study and had no trees >85-year old. Nearly 80% of the recruitment at DCD occurred in 1915–1925, whereas the primary pulse of recruitment at MCD occurred from 1935 to 1945, followed by sporadic recruitment in the 1960s and 1970s. Both pulses of recruitment were associated with periods of relatively high PDSI values (i.e., wet and cool conditions) during the dryseason (Fig. 4). The oldest trees in the study were found at Wat Chan, which showed evidence of periodic recruitment over 260 years (Fig. 3). The two stands at Wat Chan had a common period of recruitment in 1985–1995. Each stand did, however, experience recruitment independent of the other at least once (1800–1810 at BWC; 1950– 1965 at WCR). Recruitment at WCR during 1950–1965 was associated with a prolonged period of extremely high PDSI values (Fig. 4). The 1985–1995 recruitment pulse that occurred in both stands was associated with a 3-year period of positive PDSI (1988– 1990). Importantly, this was the only period of positive PDSI between 1980 and 2002, and it directly followed the most intense period (1980–1987) of sustained drought during the 20th century. The 1950–1965 recruitment pulse in the WCR stand corresponds with a period of mild dry seasons; PDSI was strongly positive during 1949–1964. Furthermore, there was a period of aboveaverage dry-season rainfall directly preceding the establishment of the cohort (1942–1950) and another in the middle of the establishment period (1954–1959). At Thung Salaeng Luang there was a site-scale pulse of pine recruitment from 1945 to 1955 (Fig. 5), a period that corresponded with two mild dry seasons (1934–1945 and 1949–1967). However, there has been no recruitment since 1960 despite several periods of above-average PDSI values. Recruitment prior to 1900 showed some overlap among pairs of stands, but was not synchronous across the three TSL stands. At Nam Nao there was a pulse of recruitment common to all three study stands in 1970–1975; however, this pulse occurred during a longer period of low to moderate recruitment from 1950 to 1990. This recruitment was associated with a prolonged period of mild dry-season conditions in which PDSI values were only slightly below average for three brief periods (1961, 1967–1968, and 1980–1982) (Fig. 5). While recruitment occurred sporadically throughout the first half of the 20th century, there is a long period from 1865 to 1910 in which no new pine trees established. Prior to 1865 there were low levels of recruitment, primarily at NAM2 and NAM3.
Fig. 3. Individual stand age structures for the four study sites: Wat Chan (stands, BWC, WCR), Chiang Dao (DCD, MCD), Nam Nao (NAM1, NAM2, NAM3) and Thung Salaeng Luang (TSL1, TSL2, TSL3).
3.2. Local and regional recruitment synchrony To test the hypothesis that climate was a primary control on historical recruitment patterns we tested for recruitment synchrony within and among sites (Table 1). We found significant statistical evidence of synchronous recruitment within three of the four study sites (Chiang Dao was the exception). In the two sites that had three sampled stands (Nam Nao and Thung Salang Luang), within-site comparisons showed that recruitment did not occur synchronously across all stands. At Nam Nao the age distributions of NAM1 and NAM2 were significantly correlated (P = 0.006), driven primarily by the common 1970–1980 cohort, but no other pairwise comparisons of stand age structures showed evidence of synchrony. The Thung Salang Luang stands were similar in having a significant correlation (P = 0.024) for one of the pairwise comparisons (TSL1 and TSL2), but none of the others despite the 1945– 1955 age cohort being common to all three stands. A pairwise comparison of site-level recruitment showed no evidence of
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Fig. 4. Climate variables and forest attributes of sites in the northern region. Hot dry-season climate variables from Mae Hong Son meteorological station (residuals, 5-year running average) are (a) total rainfall and (b) mean maximum temperature. Hot dry-season PDSI at (c) Chiang Dao and (d) Wat Chan. Chiang Dao forest attributes presented are the (e) standardised tree-ring chronology, (f) age distribution (two stands: DCD and MCD) and (g) growth releases. Wat Chan forest attributes presented are the (h) standardised tree-ring chronology, (i) age distribution (two stands: BWC and WCR) and (j) growth releases. Grey shaded areas highlight where recruitment was correlated with climate.
synchronous recruitment between any of the sites, either within regions or across regions. Nor did we find evidence of synchronous recruitment when we compared total regional recruitment between the two regions (P = 0.265).
recruitment during that period at any of the Nam Nao sites and two of the Thung Salang Luang sites. In contrast, at Thung Salang Luang PDSI values were particularly low in 1945–1950, but that 5-year period is associated with the largest number of new pine recruits during the 20th century.
3.3. Recruitment histories and climate variability 3.4. Age structures and local disturbance We used a variety of statistical tests to compare recruitment patterns with regional climate patterns. We found no significant relationships between climate (i.e., PDSI) and recruitment, regardless of whether we were comparing recruitment as a binary variable (i.e., presence/absence during any 5-year period) or as a continuous variable (i.e., total number of recruits during any 5year period). Careful examination of the recruitment and PDSI data from each site suggests that the lack of correlations may result from complex interactions among local disturbance history, regional climate patterns, and pine recruitment. In some instances recruitment was clearly associated with favourable climatic conditions. For example, recruitment pulses at Chiang Dao (DCD in 1915–1925 and MCD in 1935–1940) occurred during a prolonged period of above-average PDSI values. At Wat Chan the recruitment pulse in the late 1980s and early 1990s was associated with a period of PDSI values that were slightly above the long-term average, but were much greater than those of the preceding decade. There are, however, also instances in which recruitment did not occur during periods of favourable climatic conditions and when recruitment occurred during periods of unfavourable climatic conditions. For example, from 1905 to1930 northeastern Thailand experienced the longest run of aboveaverage PDSI values of the 20th century, yet there is little or no
The majority of growth releases at Chiang Dao and Thung Salaeng Luang occurred during periods of no recruitment (Figs. 4 and 5). At Wat Chan and Nam Nao growth releases occurred in every 5-year period from 1855 to 1995 and 1900 to present, respectively; however, most of the growth releases did not coincide with periods of recruitment (Figs. 4 and 5). 4. Discussion In tropical forests that experience an annual dry-season, drought conditions during the dry-season may act as a significant bottleneck to establishment and the long-term survival of tree seedlings. In this study we investigated the hypothesis that recruitment of new trees into tropical forests is limited to multiyear periods of relatively cool, wet conditions during the dryseason. Our results suggest that for tropical pine species P. kesiya and P. merkusii, climatic conditions during the dry-season do not appear to be the primary factor limiting recruitment. We hypothesised that if climate was the over-arching limitation to recruitment, then recruitment would be synchronous at the regional-scale (Villalba and Veblen, 1997; Barton et al., 2001; North et al., 2005; Brown, 2006). We found that recruitment was
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Fig. 5. Climate variables and forest attributes of sites in the northeastern region. Hot dry-season climate variables from Petchabuun meteorological station (residuals, 5-year running average) are (a) total rainfall and (b) mean maximum temperature. Hot dry-season PDSI at (c) Thung Salaeng Luang and (d) Nam Nao. Thung Salaeng Luang forest attributes presented are the (e) standardised tree-ring chronology, (f) age distribution (three stands: TSL1, TSL2, TSL3) and (g) growth releases. Nam Nao forest attributes presented are the (h) standardised tree-ring chronology, (i) age distribution (three stands: NAM1, NAM2, NAM3) and (j) growth releases. Grey shaded areas highlight where recruitment was correlated with climate.
asynchronous among sites and between regions (Table 1). While there was statistical evidence to support recruitment synchrony within sites, the only site in which all stands showed evidence of such recruitment patterns was Wat Chan. The other three sites either showed no evidence of synchronous recruitment (Chiang Dao) or the evidence only applied to a single pair of stands within the site (Thung Salaeng Luang, Nam Nao). Within each site the Table 1 Results of moving-blocks Monte Carlo simulations tests for synchronous recruitment Stands
1 and 2
1 and 3
2 and 3
Wat Chan Chiang Dao Nam Nao Thung Salaeng Luang
0.010 0.274 0.006 0.024
0.357 0.475
0.317 0.297
Sites
Chiang Dao
Nam Nao
Thung Salaeng Luang
Wat Chan Chiang Dao Nam Nao
0.945
0.137 0.423
0.916 0.575 0.962
Regions
Northeast
North
0.265
P-values based on comparison the observed distribution of synchronous recruitment events with 10,000 Monte Carlo simulations using randomly shuffled 20-year blocks of recruitment events (see Section 2 for details). Comparisons are made at three spatial scales: between stands within a site, between sites, and between the two regions. Comparisons were considered significant if the P-values were <0.05; significant results are shown in boldface.
sample stands were separated by a few kilometres. At this spatial scale common periods of recruitment are most likely driven by local disturbance regimes, particularly in the case of fire (Baker and Bunyavejchewin, 2009). Recruitment was distinctly synchronous within stands, however. Despite the lack of recruitment synchrony, we did find other evidence to suggest that Pinus recruitment was influenced by climatic conditions during the dry-season. In particular, we found that recruitment in the forest stands in the northern region was associated with multi-year periods of mild dry seasons. Recruitment pulses at DCD and MCD were associated with the mild dry seasons of 1917–1930 and 1936–1943, respectively. At WCR recruitment occurred during the mild dry seasons of 1949–1964 and at both WCR and BWC recruitment in the late 1980s and early 1990s was associated with favourable dry-season conditions (1988–1990) that followed a period of intense drought (1980– 1987). Similar patterns occurred in the study sites in northeastern Thailand. Recruitment at Thung Salaeng Luang corresponded with the run of mild dry seasons from 1934 to 1945 and again from 1949 to 1967. The pulse of regeneration that occurred in the early 1970s at all of the Nam Nao stands was also during a prolonged period of mild dry seasons (1949–1988). In general, recruitment at all sites was constrained to cool-wet periods in which the dry-season had mean temperatures that were below-average and mean rainfall that was above-average. No recruitment occurred during periods with below-average temperatures and below-average rainfall (i.e., cool-dry periods). While our study focuses on tropical forest recruitment, other studies have linked recruitment success in temperate forests to
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favourable climatic conditions. For example, Villalba and Veblen (1997) demonstrated that in the montane Austrocedrus forests of the southern Andes recruitment was generally associated with cool-wet conditions. However, as with our results for the Thai pines, they found that recruitment patterns were not always directly associated with variations in climate. North et al. (2005) found that recruitment in old-growth mixed conifer forests in the southern Sierra Nevada mountains was limited by interactions between local and regional factors (fire and climate). While recruitment of the two tropical pine species in northern Thailand was generally associated with mild dry seasons during the 20th century, mild dry seasons were not always associated with periods of recruitment. For example, in northeastern Thailand dry-season PDSI values were positive for the period 1907–1927, but only three trees recruited during this period at Nam Nao. In northern Thailand, the period 1949–1964 had the highest dryseason PDSI values of the 20th century, but at Chiang Dao this is the only period during the 20th century in which recruitment did not occur. Local disturbance may serve as an important precursor to recruitment, particularly for shade-intolerant tree species such as these tropical pines. Previous studies in Thailand’s tropical pine forests have suggested that fire may be an important local disturbance (e.g., Turakka et al., 1982). To determine if there was a relationship between the occurrence of local disturbance and recruitment, we used sudden growth releases in the tree-ring chronologies as a proxy indicator of disturbance. We found no correspondence between periods of sudden growth release and periods of recruitment (Figs. 4 and 5). This may be in part due to differences in the type or intensity of disturbance recorded in treering chronologies and that required to initiate recruitment. Treering chronologies typically record growth releases as a consequence of canopy disturbances that reduce crown competition (Nowacki and Abrams, 1997). In contrast, in forest ecosystems with a significant grass component on the forest floor, such as the pine savannas of Southeast Asia, low-intensity fires may generate seedbed conditions favourable for regeneration or may temporarily remove or reduce competing vegetation, but without disturbing the forest canopy (Stott, 1988). However, Koskela et al. (1995), who looked specifically at the role of fire on germination in P. merkusii, found that seedling germination and early development did was not influenced by the occurrence of fire at the site. 4.1. Limitations Inferences about the role of past climates on past recruitment events based on static age structures have their limitations (Johnson et al., 1994; Villalba and Veblen, 1997). First, determining the precise year of recruitment is complicated by dating uncertainty due to cores that miss the pith and the unknown number of years that the tree took to grow from germination to the height at which the tree cores are taken. To minimize the uncertainty associated with missing the pith, we used an established method to estimate the number of missed years in cores that were within 5 cm of the pith (Duncan, 1989; Baker et al., 2005). To account for the period of time required to grow to coring height, we added 5 years to each tree chronology based on relatively consistent evidence for this published in other studies (Turakka et al., 1982; Savage, 1994; Koskela et al., 1995). Nonetheless, the time required to grow to the sampling height is likely to vary somewhat among seedlings, particularly those suppressed by vegetation in the ground-layer, and may lead to estimation errors of several years for the establishment dates of the trees. The second limitation in determining forest recruitment patterns from static age structures is the implicit assumption
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that age structure is a function of past variation in recruitment, even though it is also influenced by past variation in mortality (Johnson et al., 1994). Further, recruitment pulses may not indicate past periods of more abundant seedling establishment, instead they may only indicate greater seedling survival (lower mortality) to the present (Villalba and Veblen, 1997). Since data on historical variations in mortality rates is difficult to obtain, we have assumed constant mortality rates. Further, the cumulative effects of mortality make recruitment periods in older cohorts more difficult to detect (Villalba and Veblen, 1997). 5. Conclusions The recruitment patterns of tropical Pinus in northern Thailand were synchronous at the stand- and site-scales, but were asynchronous at the regional-scale. Relationships between recruitment and climate were not significant; however, recruitment was associated with periods of mild, wet dry seasons. We conclude that a period of mild dry seasons may create the potential for recruitment, but this potential may not be realised if other factors, such as local disturbances, have not generated the appropriate conditions for establishment. Acknowledgements For assistance in Thailand we thank Brendan Buckley, Sarayudh Bunyavejchewin, Ed Wright, Sarah Butler and Khun Lungsuriya. We thank Andrew Robinson for statistical support and Dave Forsyth, Dale Tonkinson, Jenny Read and John Beardall for providing useful comments on this manuscript. This research was funded by the School of Biological Sciences, Monash University. This manuscript was drafted with support from the Department of Sustainability and Environment, Victoria, Australia. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2008.08.027. References Alley, W.M., 1984. The Palmer Drought Severity Index: limitations and assumptions. Journal of Climate 23, 1100–1109. Baker, P.J., Bunyavejchewn, S., Oliver, C.D., Ashton, P.S., 2005. Disturbance history and historical stand dynamics of a seasonal tropical forest in Thailand. Ecological Monographs 73, 317–343. Baker, P.J., Bunyavejchewin, S., 2009. Fire behavior and fire effects across the forest landscape of continental Southeast Asia. In: Cochrane, M. (Ed.), Tropical Fire Ecology: Climate Change, Land Use and Ecosystem Dynamics. Springer-Praxis, Heidelberg, Germany. Baker, P.J., Bunyavejchewin, S., Robinson, A.P., 2008. The impacts of large-scale, lowintensity fires on the forests of continental Southeast Asia. International Journal of Wildland Fire. in press. ˜ o-induced Surface fires in Barlow, J., Peres, C., 2004. Ecological responses to El Nin central Brazilian Amazonian: management implications for flammable tropical forests. Philosophical Transactions of the Royal Society, London (Series B) 359, 367–380. Barton, A.M., Swetnam, T.W., Baisan, C.H., 2001. Arizona pine (Pinus arizonica) stand dynamics: local and regional factors in a fire-prone madrean gallery forest of Southeast Arizona, USA. Landscape Ecology 16, 351–369. Botkin, D.B., Janak, J.F., Wallis, J.R., 1972. Rationale, limitations, and assumptions of a northeastern forest growth simulator. IBM Journal of Research and Development 16, 101–116. Brown, P., 2006. Climate effects on fire regimes and tree recruitment in Black Hills ponderosa pine forests. Ecology 87, 2500–2510. Buckley, B.M., Barbetti, M., Watanasak, M., D’Arrigo, R.D., Boonchirdchoo, S., Sarutanon, S., 1995. Dendrochronological investigations in Thailand. International Association of Wood Anatomists Journal 16, 393–409. Buckley, B.M., Cook, B.I., Bhattacharyya, A., Dukpa, D., Chaudhary, V., 2005. Global surface temperature signals in pine ring width chronologies from southern monsoon Asia. Geophysical Research Letters 32, 1–4.
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Buckley, B.M., Duangsathaporn, K., Palakit, K., Butler, S., Syhapanya, V., Xaybouangeun, N., 2007a. Analyses of growth rings of Pinus merkusii from Lao P.D.R. Forest Ecology and Management 253, 120–127. Buckley, B.M., Palakit, K., Duangsathaporn, K., Sanguantham, P., Prasomsin, P., 2007b. Decadal scale droughts over northwestern Thailand over the past 448 years: links to the tropical Pacific and Indian Ocean sectors. Climate Dynamics 29, 63–71. Bullock, S.H., Mooney, H.A., Medina, E., 1995. Seasonally Dry Tropical Forests. Cambridge University Press, New York. Carrer, M., Nola, P., Eduard, J.L., Motta, R., Urbinati, C., 2007. Regional variability of climate-growth relationships in Pinus cembra high elevation forests in the Alps. Journal of Ecology 95, 1072–1083. Clark, J.S., 1989. Effects of long-term water balances on fire regime, north-western Minnesota. Journal of Ecology 77, 989–1004. Cook, E.R., Holmes, R.L., 1985. Program ARSTAN and Users Manual. Tucson Laboratory of Tree Ring Research, University of Arizona. Cooling, E.N., 1968. Pinus merkusii. Fast Growing Timber Trees of the Lowland Tropics, vol. 4. Commonwealth Forestry Industry, Oxford. Cottam, G., Curtis, J.T., Hale, B.W., 1953. Some sampling characteristics of a population of randomly dispersed individuals. Ecology 34, 741–757. Crawley, M.J., 2005. Statistics: An Introduction using R. John Wiley and Sons, Chichester, England. Druckenbrod, D.L., 2005. Dendroecological reconstructions of forest disturbance history using time series analysis with intervention detection. Canadian Journal of Forest Research 35, 868–876. Duncan, R.P., 1989. An evaluation of errors in tree age estimates based on increment cores in kahikatea (Dacrycarpus dacryioides). New Zealand Natural Sciences 16, 31–37. Engelbrecht, B.M.J., Kursar, T.A., 2003. Comparative drought resistance of seedlings of 28 species of co-occurring tropical woody plants. Oecologia 136, 383–393. Engelbrecht, B.M.J., Dalling, J.W., Pearson, T.R.H., Wolf, R.L., Galvez, D.A., Koehler, T., Tyree, M.T., Kursar, T.A., 2006. Short dry spells in the wet season increase mortality of tropical pioneer seedlings. Oecologia 148, 258–269. Franklin, J., 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19, 474–499. Gardner, S., Sidisunthorn, P., Anusarnthorn, V., 2000. A Field Guide to the Forest Trees of Northern Thailand. Kobfai Publishing Project, Bangkok. Grissino-Mayer, H.D., 2001. Evaluation crossdating accuracy: a manual and tutorial for the computer program COFECHA. Tree-ring Research 57, 205–221. Holmes, R.L., 1983. Computer-assisted quality control in tree ring dating and measurement. Tree Ring Bulletin 43, 69–78. Johnson, E.A., Miyanashi, K., Kleb, H., 1994. The hazards of interpretation of static age structures as shown by reconstructions in a Pinus contorta—Pinus engelmanii forest. Journal of Ecology 82, 923–931. Koskela, J., Kussipalo, J., Sirikul, W., 1995. Natural regeneration dynamics of Pinus merkusii in northern Thailand. Forest Ecology and Management 77, 169–179. Laurance, W.F., 2003. Slow burn: the insidious effects of surface fires on tropical forests. Trends in Ecology and Evolution 18, 209–212.
Lorimer, C.G., Frelich, L.E., 1989. A methodology for estimating canopy disturbance frequency and intensity in dense temperate forests. Canadian Journal of Forest Research 19, 651–663. North, M., Hurteau, M., Fiegener, R., Barbour, M., 2005. Influence of fire and El Nin´o on tree recruitment varies by species in a Sierran Mixed Conifer. Forest Science 51, 187–197. Nowacki, G.J., Abrams, M.D., 1997. Radial growth averaging criteria for reconstructing disturbance histories from pre-settlement origin oaks. Ecological Monographs 67, 225–246. Oliver, C.D., Larson, B.C., 1996. Forest Stand Dynamics. John Wiley and Sons, Inc., New York. Pacala, S.W., Canham, C.D., Silander, J.A.J., 1993. Forest models defined by field measurements. I. The design of a northeastern forest simulator. Canadian Journal of Forest Research 23, 1980–1988. Pacala, S.W., Canham, C.D., Silander, J.A.J., Kobe, R.K., Ribbens, E., 1996. Forest models defined by field measurements: estimation, error analysis and dynamics. Ecological Monographs 66, 1–43. Pearson, T.R.H., Burslem, D.F.R.P., Goeriz, R.E., Dalling, J.W., 2003. Regeneration niche partitioning in neotropical pioneers: effects of gap size, seasonal drought and herbivory on growth and survival. Oecologia 137, 456–465. Prentice, I.C., Cramer, W., Harrison, S.P., Leemans, R., Monserud, R.A., Solomon, A.M., 1992. A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography 19, 117–134. Pumijumnong, N., Wanyaphet, T., 2006. Seasonal cambial activity and tree ring formation in Pinus merkusii and Pinus kesiya in Northern Thailand in dependence on climate. Forest Ecology and Management 226, 279–289. R Development Core Team, 2005. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL: http://www.R-project.org, ISBN: 3-900051-07-0. Savage, M., 1994. Land-use change and the structural dynamics of Pinus kesiya in a hill evergreen forest in northern Thailand. Mountain Research and Development 14, 245–350. Slot, M., Poorter, L., 2007. Diversity of tropical tree seedling responses to drought. Biotropica 39, 683–690. Stokes, M.A., Smiley, T.L., 1968. An Introduction to Tree Ring Dating. University of Chicago, Chicago. Stott, P., 1988. The forest as phoenix: towards a biogeography of fire in mainland South East Asia. Geographical Journal 154, 337–350. Troup, R.S., 1921. Silviculture of Indian trees, vol. 1. Clarendon Press, Oxford. Turakka, A., Luukkanen, O., Bhumbihamon, S., 1982. Notes on Pinus kesiya and P. merkusii and their natural regeneration in watershed areas of northern Thailand. Acta Forestalia Fennica 178, 1–31. van der Schrier, G., Briffa, K.R., Jones, P.D., Osborn, T.J., 2005. Summer moisture availability across Europe. Journal of Climate 19, 2818–2834. Villalba, R., Veblen, T.T., 1997. Regional patterns of tree population age structures in Northern Patagonia: climatic and disturbance influences. Journal of Ecology 85, 113–124. Westerling, A.L., Hidalgo, H.G., Cayan, D.R., Swetnam, T.W., 2006. Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313, 940–943.
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Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco
Climate and historical stand dynamics in the tropical pine forests of northern Thailand Heidi Zimmer *, Patrick Baker School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 May 2008 Received in revised form 23 August 2008 Accepted 25 August 2008
Forest recruitment is the outcome of local- and regional-scale factors such as disturbances and climate. The relative importance of local- and regional-scale factors will determine the spatial scale at which temporal pulses of recruitment occur. In seasonal tropical forests, where the annual dry-season is a critical bottleneck to seedling survival, multi-year periods of relatively cool, wet dry seasons may be required for successful tree recruitment. Consequently, when such conditions are present, region-wide synchronisation of recruitment may occur. To examine the case for regional synchronisation of forest dynamics in the seasonal tropical pine forests of northern Thailand, we investigated forest age structures at three spatial scales: stand, site and region. We compared forest age structures with instrumental climatic records beginning in 1902. We found significant statistical evidence of synchronous recruitment at the stand- and site-scales, but not at the regional-scale. While correlations between recruitment and climate were not statistically significant, recruitment success was often linked to favourable climatic conditions. For example, recruitment at all sites was associated with multi-year periods of cool-wet dry seasons. The lack of significant correlations between recruitment and climate appears to reflect complex interactions among local disturbance history, regional climate variability and pine recruitment. ß 2008 Elsevier B.V. All rights reserved.
Keywords: Dendrochronology Pinus kesiya Pinus merkusii Regional synchrony Tropical forest recruitment
1. Introduction The successful recruitment of new trees in a forest is a major determinant of long-term forest dynamics and the distribution of local and regional forest types. In any forest, recruitment is influenced by processes that occur across a range of spatial and temporal scales (Oliver and Larson, 1996). At regional and global scales, the roles of climate and geology on plant distributions are well-documented (Prentice et al., 1992; Franklin, 1995). Within individual stands and across landscapes, disturbances and biotic interactions are important influences on regeneration success, forest dynamics, and the relative local abundance of tree species (Botkin et al., 1972; Pacala et al., 1993, 1996). Interactions across scales may also be important in determining forest dynamics. Local-scale processes, such as disturbances, may be strongly influenced by processes at larger scales, such as climate. For example, the occurrence of landscape-scale fires is directly influenced by regional climate dynamics (e.g., Clark,
* Corresponding author. Present address: Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, PO Box 137, Heidelberg, VIC 3084, Australia. Tel.: +61 3 9450 8648; fax: +61 3 9450 8799. E-mail address: [email protected] (H. Zimmer). 0378-1127/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2008.08.027
1989; Westerling et al., 2006). Regional-scale, supra-annual climate events, such as ENSO-induced droughts, may also influence seedling recruitment by making the environmental conditions for germination and establishment harsher than usual. For tree species in which recruitment is disturbance-dependent, successful recruitment may require the synchronous occurrence of a disturbance and the appropriate environmental conditions. In seasonal tropical forests, the dry-season drought is a significant source of mortality for seedlings of many tree species (Troup, 1921; Engelbrecht and Kursar, 2003; Pearson et al., 2003). High temperatures and low relative humidity generate high vapour pressure deficits, which lead to severe water stress in seedlings, particularly small seedlings that do not have wellestablished root systems (Engelbrecht et al., 2006; Slot and Poorter, 2007). In addition, these same microclimatic conditions make seasonal tropical forests prone to fire. While fires in seasonal tropical forests are often low-intensity surface fires (Laurance, 2003; Barlow and Peres, 2004), mortality among seedlings is typically high (Baker et al., 2008). Consequently, successful recruitment of trees in seasonal tropical environments may depend on the occurrence of 3–4 years of mild (i.e. cool-wet) dry seasons (Troup, 1921; Bullock et al., 1995). Where regional climatic conditions are important in determining recruitment success and the occurrence of disturbances at the
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local-scale, regional synchronisation of forest dynamics may occur. While the relative importance of local and regional processes on forest dynamics is not well understood, several recent papers have provided evidence of regionally synchronised dynamics in temperate forests (Villalba and Veblen, 1997; Barton et al., 2001; North et al., 2005; Brown, 2006; Carrer et al., 2007). For example, Barton et al. (2001) demonstrated that large-scale climatic patterns in southwestern North America influence moisture availability and fire occurrence, which determine tree age structures, as well as growth and mortality rates. In Patagonia, Villalba and Veblen (1997) showed that seedling survival was higher during cool-wet periods and lower during warm-dry periods, leading to strong correlations between decadalscale climate variation over the past 40 years and forest age structure. In this paper we examine the case for regional synchronisation of forest dynamics in low-diversity tropical pine forests in continental Thailand. To do this, we used reconstructed pine recruitment histories to examine patterns of recruitment synchronisation at the stand-, site-, and regional-scales and compared them to local and regional instrumental records. The two pine species that are commonly found in these forests, Pinus kesiya Royle ex Gordon and Pinus merkusii Jungh et De Vriese, both form reliable annual growth rings, are sensitive to climate and have been the subject of recent dendroclimatic investigations in northern Thailand and Laos (Buckley et al., 1995, 2005, 2007a,b; Pumijumnong and Wanyaphet, 2006). We hypothesised that if climate was the primary limitation to recruitment, then recruitment should be synchronous on a regional-scale and associated with periods of relatively mild climatic conditions. In contrast, if recruitment is primarily a function of local disturbances then recruitment should be synchronous at the stand-scale, but asynchronous at site and regional-scales and not necessarily associated with mild climatic conditions. 2. Methods 2.1. Study area and species To determine the relative influence of local and regional processes in recruitment success of tropical pine forests in continental Southeast Asia, we established a spatially nested sampling strategy with replicated study plots at stand, site, and regional-scales. In total we assessed recruitment dynamics at 10 tropical pine forest stands at four sites within the northern and northeastern regions of Thailand (Fig. 1). The four study sites were Doi Chiang Dao Wildlife Sanctuary (two stands: DCD and MCD), Wat Chan (two stands: BWC and WCR), Thung Salaeng Luang National Park (three stands: TSL1-3) and Nam Nao National Park (three stands: NAM1-3). In northern Thailand, P. kesiya occurs at high elevations (1000– 1700 m) on exposed ridges with sandy soils (Gardner et al., 2000) and P. merkusii occurs at lower elevations, usually below 1000 m (Gardner et al., 2000) on poor, well-drained soils (Turakka et al., 1982). P. kesiya generally grows faster and has a shorter lifespan than P. merkusii. Both species produce cones annually (Cooling, 1968), but little is known of their recruitment dynamics. Koskela et al. (1995) reported that young P. merkusii grow in a grass stage characterised by thick stems and little growth in height, during which seedlings are less vulnerable to fire (Cooling, 1968). In contrast, P. kesiya seedlings start shoot growth immediately after germination and reach their height of fire resistance, 2.5 m, at 5 years. P. kesiya also has a shallower taproot and smaller seeds. These characteristics may make P. kesiya more susceptible to drought stress (Turakka et al., 1982).
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2.2. Sampling design At each of the four sites we identified 2–3 representative stands that were 1–10 km apart. We established 2–5 transects in each stand, with the starting point of the first transect chosen subjectively, depending on the size and heterogeneity of the stand. At randomly selected distances along the transect, we established 2–6 study plots (typically 20–60 m apart). We used the pointcentred quarters method (Cottam et al., 1953) to identify four sample trees in each plot, and to estimate stand density and basal area. We measured the distance from the plot centre to the nearest tree <10 cm diameter at breast height (DBH; 1.35 m) in each of the four quadrats. For each of these trees we measured DBH and recorded species identity. Two cores were extracted from each pine tree at breast height. If any core was judged to have missed the pith by >5 cm, it was discarded and another core was taken. We added plots to each transect until we had sampled a minimum number of 20 pines within each stand. 2.3. Chronology development and standardisation All cores were prepared following standard dendrochronological procedures (Stokes and Smiley, 1968). Cores taken from the same stand were visually crossdated to ensure accuracy in the allocation of annual growth rings to the year in which each was produced. This was particularly important for P. merkusii, which frequently produces false rings (Buckley et al., 1995; Fig. S1 in Supplementary Material). We analysed the preliminary visual crossdating using the program COFECHA (Holmes, 1983; GrissinoMayer, 2001), which calculates mean inter-series correlations between each individual ring-width series and a mean chronology. Mean chronologies were generated for each stand and site from all ring-width series at the respective sample level. Some individual ring-width series showed irregular growth patterns and/or unclear ring structure, and correlated poorly with the mean chronology. These series were re-checked and either corrected or removed from further analyses. The remaining set of ring-width series was then standardised to create a master chronology for each stand and site. We used the program ARSTAN (Cook and Holmes, 1985) to standardise the chronologies. Ring-width values were transformed into standard values with a mean of 1 and with stable variance using an adaptive power transformation (Druckenbrod, 2005). We did this to ensure that our ring-width series met the assumptions of normality and equal variance required for subsequent regression analyses with climate variables. The transformed series were de-trended using a negative exponential curve or a straight line to remove any age-related growth trend. The rejection rate of tree core samples due to dating problems was relatively low for tropical tree species. Of the 273 trees that we sampled, 231 (84%) were included in the final analyses of age structure and recruitment synchrony. 2.4. Relating age structures to climate Tree ages were derived from the crossdated tree-ring series. Where a core did not intersect the pith of a sample tree, the missing distance to the pith was estimated using Duncan’s geometric method (Duncan, 1989). This estimated distance was then divided by the average ring-width of the five rings closest to the pith, resulting in an estimate of the number of missing years between the last ring on the core and the missed pith. To account for the number of years required for the trees to grow to 1.35 m (i.e., coring height) we added 5 years to the pith ages obtained from the tree cores. Although the numbers of years
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Fig. 1. Locations of the four study sites in northern and northeastern Thailand.
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required to grow to breast height may vary, the only published studies (Turakka et al., 1982; Savage, 1994, Koskela et al., 1995) on the recruitment of these pines all suggest that seedlings require an average of 4–6 years to reach breast height. Age structure diagrams were created for each stand (n = 10), site (n = 4), and region (n = 2) (see Section 2.1). Establishment dates were grouped into 5-year periods (e.g., 1900–1904); for brevity we refer to trees establishing in a 5-year period by the first year of that period (e.g., 1900 represents 1900–1904). To determine if establishment was correlated with environmental conditions, we compared climate data to the age structure results at each spatial scale. To test the hypothesis that climate variations influence tree recruitment in these seasonal tropical forests (e.g., Troup, 1921), we compared dry-season (February–April, Fig. 2) climate data, expressed as 5-year running averages of the deviations from the long-term mean values, with the age structure diagrams. Climate data for each of the four sites consisted of monthly records for total rainfall, number of rainy days and minimum and maximum mean temperatures. Instrumental climate records for Chiang Dao and Wat Chan were taken from the Mae Hong Son meteorological station (198160 N, 978560 E), approximately 126 km and 37 km away from the sites, respectively. Instrumental climate records for Thung Salaeng Luang and Nam Nao were taken from the Petchabun meteorological station (168250 N, 1018080 E), approximately 30 km and 58 km away from the sites, respectively. Mean annual precipitation at Mae Hong Son was 1264 mm and at Petchabun was 1122 mm. From November to March both stations record a mean of <50 mm rainfall (Fig. 2). Mean temperature (January/July) at Mae Hong Son was 20.5/26.8 8C and at Petchabun was 24.3/27.7 8C. Instrumental climate data covered the periods 1911–2002 (Mae Hong Son precipitation) and 1951–2006 (Mae Hong Son and Petchabun temperatures and Petchabun precipita-
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tion). As a measure of water available to plants we used the Palmer Drought Severity Index (PDSI), which estimates drought severity using a water-balance equation (Alley, 1984). We used the selfcalibrating PDSI 0.58 0.58 gridded dataset derived from the algorithms developed in van der Schrier et al. (2005) for grid cells centred on each of the sites; 198250 N, 988750 E (Chiang Dao), 198250 N, 988250 E (Wat Chan), 168750 N, 1008750 E (Thung Salaeng Luang), and 168750 N, 1008250 E (Nam Nao). PDSI data for each site were available for the periods 1901–2002. To compare recruitment to climate data we used a general linear model (GLM) framework. Because recruitment data are based on counts and are bounded by zero, simple correlations or linear regressions of climate against recruitment would be inappropriate because the count data violate the distributional assumptions of these approaches. We used two different approaches within the GLM framework to account for this. First, we used the raw count data and specified a poisson error structure appropriate for such data for the GLM. However, because our data were overdispersed (i.e., residual deviance >degrees of freedom), we used a quasipoisson error structure in all of our analyses (Crawley, 2005). Second, we converted the recruitment data to a binary variable (i.e., presence/absence of recruitment) and used a binomial error structure for the GLM. In both cases we considered the models to be significant if the parameter estimates had a < 0.05. 2.5. Analysis of recruitment synchrony To determine if recruitment was synchronous at various spatial scales, we used a moving-blocks Monte Carlo simulation approach. We treated recruitment at each stand as a binary variable in which each year in each chronology was assigned a ‘1’ if recruitment had occurred or a ‘0’ if it had not. To compare the recruitment periods between two sites the values (either 0 or 1) for each year were multiplied together. Where synchronous recruitment occurs, a value of 1 is returned; where no recruitment occurs or recruitment only occurs in one site, a value of zero is returned. The number of occurrences of synchronous recruitment for a given period of years (i.e., block length) can be assumed in the absence of temporal autocorrelation to follow a hypergeometric distribution. To test for recruitment synchrony, we compared the frequency distribution of synchronous recruitment events that occurred in a pair of sites to 10,000 Monte Carlo simulations in which blocks of contiguous 20year periods are reordered randomly from a randomly selected start year. While the dataset included trees that established as early as 1741, we conducted our analyses for the periods 1800– 2005, as prior to 1800 only three trees recruited. We compared recruitment among the two to three stands within each site, among the four sites, and between the two regions, with the randomised data, to test the hypothesis that recruitment happened in non-random synchronous pulses. We conducted all analyses in the statistical program R, version 2.6.2 (R Development Core Team, 2005). 2.6. Recruitment and local factors
Fig. 2. Mean monthly rainfall and temperature recorded at (a) Mae Hong Son meteorological station (northern Thailand) and (b) Petchabun meteorological station (northeastern Thailand).
To assess the role of local disturbance on recruitment dynamics, we analysed growth releases from individual tree-ring records to provide a proxy measure of local disturbance occurrence. These were compared with the timing of recruitment periods and the climate records. Growth releases were identified using the percent growth change method (Nowacki and Abrams, 1997) implemented in the stand dynamics sub-routine of ARSTAN (Cook and Holmes, 1985). The individual chronologies were examined using a 10-year running average that compared average ring-widths for 10 years
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prior to, and 10 years after, a given year to ascertain whether a change in growth rate had occurred. Percent growth change was used to determine the size of the growth release: a 100% increase was considered to represent a major release and a 50–99% increase represented a moderate release (Lorimer and Frelich, 1989). 3. Results 3.1. Recruitment histories Among the 231 trees that we sampled in north and northeastern Thailand, ages ranged from 12 to 269 years old for P. kesiya and 12 to 234 years old for P. merkusii. Recruitment varied considerably within and among stands (Fig. 3). Most of our study stands had two or more age cohorts. In some stands (e.g., TSL1-3, NAM2) there were two distinct age cohorts. In other stands (e.g., both Wat Chan sites, NAM3, MCD) there was only one discrete age cohort, but other older or younger trees were scattered in low numbers across the age distribution. One of the Chiang Dao stands (DCD) was unique in having only a single age cohort. The two Chiang Dao stands, DCD and MCD, were the youngest in our study and had no trees >85-year old. Nearly 80% of the recruitment at DCD occurred in 1915–1925, whereas the primary pulse of recruitment at MCD occurred from 1935 to 1945, followed by sporadic recruitment in the 1960s and 1970s. Both pulses of recruitment were associated with periods of relatively high PDSI values (i.e., wet and cool conditions) during the dryseason (Fig. 4). The oldest trees in the study were found at Wat Chan, which showed evidence of periodic recruitment over 260 years (Fig. 3). The two stands at Wat Chan had a common period of recruitment in 1985–1995. Each stand did, however, experience recruitment independent of the other at least once (1800–1810 at BWC; 1950– 1965 at WCR). Recruitment at WCR during 1950–1965 was associated with a prolonged period of extremely high PDSI values (Fig. 4). The 1985–1995 recruitment pulse that occurred in both stands was associated with a 3-year period of positive PDSI (1988– 1990). Importantly, this was the only period of positive PDSI between 1980 and 2002, and it directly followed the most intense period (1980–1987) of sustained drought during the 20th century. The 1950–1965 recruitment pulse in the WCR stand corresponds with a period of mild dry seasons; PDSI was strongly positive during 1949–1964. Furthermore, there was a period of aboveaverage dry-season rainfall directly preceding the establishment of the cohort (1942–1950) and another in the middle of the establishment period (1954–1959). At Thung Salaeng Luang there was a site-scale pulse of pine recruitment from 1945 to 1955 (Fig. 5), a period that corresponded with two mild dry seasons (1934–1945 and 1949–1967). However, there has been no recruitment since 1960 despite several periods of above-average PDSI values. Recruitment prior to 1900 showed some overlap among pairs of stands, but was not synchronous across the three TSL stands. At Nam Nao there was a pulse of recruitment common to all three study stands in 1970–1975; however, this pulse occurred during a longer period of low to moderate recruitment from 1950 to 1990. This recruitment was associated with a prolonged period of mild dry-season conditions in which PDSI values were only slightly below average for three brief periods (1961, 1967–1968, and 1980–1982) (Fig. 5). While recruitment occurred sporadically throughout the first half of the 20th century, there is a long period from 1865 to 1910 in which no new pine trees established. Prior to 1865 there were low levels of recruitment, primarily at NAM2 and NAM3.
Fig. 3. Individual stand age structures for the four study sites: Wat Chan (stands, BWC, WCR), Chiang Dao (DCD, MCD), Nam Nao (NAM1, NAM2, NAM3) and Thung Salaeng Luang (TSL1, TSL2, TSL3).
3.2. Local and regional recruitment synchrony To test the hypothesis that climate was a primary control on historical recruitment patterns we tested for recruitment synchrony within and among sites (Table 1). We found significant statistical evidence of synchronous recruitment within three of the four study sites (Chiang Dao was the exception). In the two sites that had three sampled stands (Nam Nao and Thung Salang Luang), within-site comparisons showed that recruitment did not occur synchronously across all stands. At Nam Nao the age distributions of NAM1 and NAM2 were significantly correlated (P = 0.006), driven primarily by the common 1970–1980 cohort, but no other pairwise comparisons of stand age structures showed evidence of synchrony. The Thung Salang Luang stands were similar in having a significant correlation (P = 0.024) for one of the pairwise comparisons (TSL1 and TSL2), but none of the others despite the 1945– 1955 age cohort being common to all three stands. A pairwise comparison of site-level recruitment showed no evidence of
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Fig. 4. Climate variables and forest attributes of sites in the northern region. Hot dry-season climate variables from Mae Hong Son meteorological station (residuals, 5-year running average) are (a) total rainfall and (b) mean maximum temperature. Hot dry-season PDSI at (c) Chiang Dao and (d) Wat Chan. Chiang Dao forest attributes presented are the (e) standardised tree-ring chronology, (f) age distribution (two stands: DCD and MCD) and (g) growth releases. Wat Chan forest attributes presented are the (h) standardised tree-ring chronology, (i) age distribution (two stands: BWC and WCR) and (j) growth releases. Grey shaded areas highlight where recruitment was correlated with climate.
synchronous recruitment between any of the sites, either within regions or across regions. Nor did we find evidence of synchronous recruitment when we compared total regional recruitment between the two regions (P = 0.265).
recruitment during that period at any of the Nam Nao sites and two of the Thung Salang Luang sites. In contrast, at Thung Salang Luang PDSI values were particularly low in 1945–1950, but that 5-year period is associated with the largest number of new pine recruits during the 20th century.
3.3. Recruitment histories and climate variability 3.4. Age structures and local disturbance We used a variety of statistical tests to compare recruitment patterns with regional climate patterns. We found no significant relationships between climate (i.e., PDSI) and recruitment, regardless of whether we were comparing recruitment as a binary variable (i.e., presence/absence during any 5-year period) or as a continuous variable (i.e., total number of recruits during any 5year period). Careful examination of the recruitment and PDSI data from each site suggests that the lack of correlations may result from complex interactions among local disturbance history, regional climate patterns, and pine recruitment. In some instances recruitment was clearly associated with favourable climatic conditions. For example, recruitment pulses at Chiang Dao (DCD in 1915–1925 and MCD in 1935–1940) occurred during a prolonged period of above-average PDSI values. At Wat Chan the recruitment pulse in the late 1980s and early 1990s was associated with a period of PDSI values that were slightly above the long-term average, but were much greater than those of the preceding decade. There are, however, also instances in which recruitment did not occur during periods of favourable climatic conditions and when recruitment occurred during periods of unfavourable climatic conditions. For example, from 1905 to1930 northeastern Thailand experienced the longest run of aboveaverage PDSI values of the 20th century, yet there is little or no
The majority of growth releases at Chiang Dao and Thung Salaeng Luang occurred during periods of no recruitment (Figs. 4 and 5). At Wat Chan and Nam Nao growth releases occurred in every 5-year period from 1855 to 1995 and 1900 to present, respectively; however, most of the growth releases did not coincide with periods of recruitment (Figs. 4 and 5). 4. Discussion In tropical forests that experience an annual dry-season, drought conditions during the dry-season may act as a significant bottleneck to establishment and the long-term survival of tree seedlings. In this study we investigated the hypothesis that recruitment of new trees into tropical forests is limited to multiyear periods of relatively cool, wet conditions during the dryseason. Our results suggest that for tropical pine species P. kesiya and P. merkusii, climatic conditions during the dry-season do not appear to be the primary factor limiting recruitment. We hypothesised that if climate was the over-arching limitation to recruitment, then recruitment would be synchronous at the regional-scale (Villalba and Veblen, 1997; Barton et al., 2001; North et al., 2005; Brown, 2006). We found that recruitment was
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Fig. 5. Climate variables and forest attributes of sites in the northeastern region. Hot dry-season climate variables from Petchabuun meteorological station (residuals, 5-year running average) are (a) total rainfall and (b) mean maximum temperature. Hot dry-season PDSI at (c) Thung Salaeng Luang and (d) Nam Nao. Thung Salaeng Luang forest attributes presented are the (e) standardised tree-ring chronology, (f) age distribution (three stands: TSL1, TSL2, TSL3) and (g) growth releases. Nam Nao forest attributes presented are the (h) standardised tree-ring chronology, (i) age distribution (three stands: NAM1, NAM2, NAM3) and (j) growth releases. Grey shaded areas highlight where recruitment was correlated with climate.
asynchronous among sites and between regions (Table 1). While there was statistical evidence to support recruitment synchrony within sites, the only site in which all stands showed evidence of such recruitment patterns was Wat Chan. The other three sites either showed no evidence of synchronous recruitment (Chiang Dao) or the evidence only applied to a single pair of stands within the site (Thung Salaeng Luang, Nam Nao). Within each site the Table 1 Results of moving-blocks Monte Carlo simulations tests for synchronous recruitment Stands
1 and 2
1 and 3
2 and 3
Wat Chan Chiang Dao Nam Nao Thung Salaeng Luang
0.010 0.274 0.006 0.024
0.357 0.475
0.317 0.297
Sites
Chiang Dao
Nam Nao
Thung Salaeng Luang
Wat Chan Chiang Dao Nam Nao
0.945
0.137 0.423
0.916 0.575 0.962
Regions
Northeast
North
0.265
P-values based on comparison the observed distribution of synchronous recruitment events with 10,000 Monte Carlo simulations using randomly shuffled 20-year blocks of recruitment events (see Section 2 for details). Comparisons are made at three spatial scales: between stands within a site, between sites, and between the two regions. Comparisons were considered significant if the P-values were <0.05; significant results are shown in boldface.
sample stands were separated by a few kilometres. At this spatial scale common periods of recruitment are most likely driven by local disturbance regimes, particularly in the case of fire (Baker and Bunyavejchewin, 2009). Recruitment was distinctly synchronous within stands, however. Despite the lack of recruitment synchrony, we did find other evidence to suggest that Pinus recruitment was influenced by climatic conditions during the dry-season. In particular, we found that recruitment in the forest stands in the northern region was associated with multi-year periods of mild dry seasons. Recruitment pulses at DCD and MCD were associated with the mild dry seasons of 1917–1930 and 1936–1943, respectively. At WCR recruitment occurred during the mild dry seasons of 1949–1964 and at both WCR and BWC recruitment in the late 1980s and early 1990s was associated with favourable dry-season conditions (1988–1990) that followed a period of intense drought (1980– 1987). Similar patterns occurred in the study sites in northeastern Thailand. Recruitment at Thung Salaeng Luang corresponded with the run of mild dry seasons from 1934 to 1945 and again from 1949 to 1967. The pulse of regeneration that occurred in the early 1970s at all of the Nam Nao stands was also during a prolonged period of mild dry seasons (1949–1988). In general, recruitment at all sites was constrained to cool-wet periods in which the dry-season had mean temperatures that were below-average and mean rainfall that was above-average. No recruitment occurred during periods with below-average temperatures and below-average rainfall (i.e., cool-dry periods). While our study focuses on tropical forest recruitment, other studies have linked recruitment success in temperate forests to
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favourable climatic conditions. For example, Villalba and Veblen (1997) demonstrated that in the montane Austrocedrus forests of the southern Andes recruitment was generally associated with cool-wet conditions. However, as with our results for the Thai pines, they found that recruitment patterns were not always directly associated with variations in climate. North et al. (2005) found that recruitment in old-growth mixed conifer forests in the southern Sierra Nevada mountains was limited by interactions between local and regional factors (fire and climate). While recruitment of the two tropical pine species in northern Thailand was generally associated with mild dry seasons during the 20th century, mild dry seasons were not always associated with periods of recruitment. For example, in northeastern Thailand dry-season PDSI values were positive for the period 1907–1927, but only three trees recruited during this period at Nam Nao. In northern Thailand, the period 1949–1964 had the highest dryseason PDSI values of the 20th century, but at Chiang Dao this is the only period during the 20th century in which recruitment did not occur. Local disturbance may serve as an important precursor to recruitment, particularly for shade-intolerant tree species such as these tropical pines. Previous studies in Thailand’s tropical pine forests have suggested that fire may be an important local disturbance (e.g., Turakka et al., 1982). To determine if there was a relationship between the occurrence of local disturbance and recruitment, we used sudden growth releases in the tree-ring chronologies as a proxy indicator of disturbance. We found no correspondence between periods of sudden growth release and periods of recruitment (Figs. 4 and 5). This may be in part due to differences in the type or intensity of disturbance recorded in treering chronologies and that required to initiate recruitment. Treering chronologies typically record growth releases as a consequence of canopy disturbances that reduce crown competition (Nowacki and Abrams, 1997). In contrast, in forest ecosystems with a significant grass component on the forest floor, such as the pine savannas of Southeast Asia, low-intensity fires may generate seedbed conditions favourable for regeneration or may temporarily remove or reduce competing vegetation, but without disturbing the forest canopy (Stott, 1988). However, Koskela et al. (1995), who looked specifically at the role of fire on germination in P. merkusii, found that seedling germination and early development did was not influenced by the occurrence of fire at the site. 4.1. Limitations Inferences about the role of past climates on past recruitment events based on static age structures have their limitations (Johnson et al., 1994; Villalba and Veblen, 1997). First, determining the precise year of recruitment is complicated by dating uncertainty due to cores that miss the pith and the unknown number of years that the tree took to grow from germination to the height at which the tree cores are taken. To minimize the uncertainty associated with missing the pith, we used an established method to estimate the number of missed years in cores that were within 5 cm of the pith (Duncan, 1989; Baker et al., 2005). To account for the period of time required to grow to coring height, we added 5 years to each tree chronology based on relatively consistent evidence for this published in other studies (Turakka et al., 1982; Savage, 1994; Koskela et al., 1995). Nonetheless, the time required to grow to the sampling height is likely to vary somewhat among seedlings, particularly those suppressed by vegetation in the ground-layer, and may lead to estimation errors of several years for the establishment dates of the trees. The second limitation in determining forest recruitment patterns from static age structures is the implicit assumption
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that age structure is a function of past variation in recruitment, even though it is also influenced by past variation in mortality (Johnson et al., 1994). Further, recruitment pulses may not indicate past periods of more abundant seedling establishment, instead they may only indicate greater seedling survival (lower mortality) to the present (Villalba and Veblen, 1997). Since data on historical variations in mortality rates is difficult to obtain, we have assumed constant mortality rates. Further, the cumulative effects of mortality make recruitment periods in older cohorts more difficult to detect (Villalba and Veblen, 1997). 5. Conclusions The recruitment patterns of tropical Pinus in northern Thailand were synchronous at the stand- and site-scales, but were asynchronous at the regional-scale. Relationships between recruitment and climate were not significant; however, recruitment was associated with periods of mild, wet dry seasons. We conclude that a period of mild dry seasons may create the potential for recruitment, but this potential may not be realised if other factors, such as local disturbances, have not generated the appropriate conditions for establishment. Acknowledgements For assistance in Thailand we thank Brendan Buckley, Sarayudh Bunyavejchewin, Ed Wright, Sarah Butler and Khun Lungsuriya. We thank Andrew Robinson for statistical support and Dave Forsyth, Dale Tonkinson, Jenny Read and John Beardall for providing useful comments on this manuscript. This research was funded by the School of Biological Sciences, Monash University. This manuscript was drafted with support from the Department of Sustainability and Environment, Victoria, Australia. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2008.08.027. References Alley, W.M., 1984. The Palmer Drought Severity Index: limitations and assumptions. Journal of Climate 23, 1100–1109. Baker, P.J., Bunyavejchewn, S., Oliver, C.D., Ashton, P.S., 2005. Disturbance history and historical stand dynamics of a seasonal tropical forest in Thailand. Ecological Monographs 73, 317–343. Baker, P.J., Bunyavejchewin, S., 2009. Fire behavior and fire effects across the forest landscape of continental Southeast Asia. In: Cochrane, M. (Ed.), Tropical Fire Ecology: Climate Change, Land Use and Ecosystem Dynamics. Springer-Praxis, Heidelberg, Germany. Baker, P.J., Bunyavejchewin, S., Robinson, A.P., 2008. The impacts of large-scale, lowintensity fires on the forests of continental Southeast Asia. International Journal of Wildland Fire. in press. ˜ o-induced Surface fires in Barlow, J., Peres, C., 2004. Ecological responses to El Nin central Brazilian Amazonian: management implications for flammable tropical forests. Philosophical Transactions of the Royal Society, London (Series B) 359, 367–380. Barton, A.M., Swetnam, T.W., Baisan, C.H., 2001. Arizona pine (Pinus arizonica) stand dynamics: local and regional factors in a fire-prone madrean gallery forest of Southeast Arizona, USA. Landscape Ecology 16, 351–369. Botkin, D.B., Janak, J.F., Wallis, J.R., 1972. Rationale, limitations, and assumptions of a northeastern forest growth simulator. IBM Journal of Research and Development 16, 101–116. Brown, P., 2006. Climate effects on fire regimes and tree recruitment in Black Hills ponderosa pine forests. Ecology 87, 2500–2510. Buckley, B.M., Barbetti, M., Watanasak, M., D’Arrigo, R.D., Boonchirdchoo, S., Sarutanon, S., 1995. Dendrochronological investigations in Thailand. International Association of Wood Anatomists Journal 16, 393–409. Buckley, B.M., Cook, B.I., Bhattacharyya, A., Dukpa, D., Chaudhary, V., 2005. Global surface temperature signals in pine ring width chronologies from southern monsoon Asia. Geophysical Research Letters 32, 1–4.
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