Growth responses of understorey trees to drought perturbation in tropical rainforest in Borneo

Forest Ecology and Management 262 (2011) 2095–2107

Contents lists available at SciVerse ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Growth responses of understorey trees to drought perturbation in tropical rainforest in Borneo D.M. Newbery a,⇑, M. Lingenfelder b, K.F. Poltz a, R.C. Ong c, C.E. Ridsdale d a

Institute of Plant Sciences, University of Bern, Altenbergrain 21, 3013 Bern, Switzerland Department of Forest Biometry, University of Freiburg, Tennenbacher Strasse 4, 79085 Freiburg, Germany c Forest Research Centre, Sabah Forestry Department, P.O. Box 1407, 90715 Sandakan, Sabah, Malaysia d National Herbarium of the Netherlands, van Steenis Gebouw, Einsteinweg 2, P.O. Box 9514, 2300 RA Leiden, The Netherlands b

a r t i c l e

i n f o

Article history: Received 13 May 2011 Received in revised form 21 July 2011 Accepted 25 July 2011 Available online 23 September 2011 Keywords: Borneo Drought Plurality Coloured noise Stochasticity Understorey

a b s t r a c t At Danum Valley, Sabah, dipterocarp forest is affected by moderately-strong droughts which perturb the ecosystem. Analysing stem growth for c. 3700 understorey trees (12.5–<50 cm girth), measured over four periods (between 1986 and 2007), response to an ENSO-related event (1998) was followed. Relative growth rates (rgr) of the 48 most abundant species in the size class were considered individually, and as relative changes between periods. From them a measure reactivity was derived. Whilst a third of species differed from one another in rgr, within-species rates were highly variable: often species had very different (pluralistic) response patterns over time. The rgr decreased in the drought period, increased and overcompensated directly afterwards, and later returned to original levels. The forest displayed moderate resistance, and high resilience and stability within c. 4 years of the perturbation. Oscillatory responses were more pronounced among true understorey species than among small trees of overstorey ones, suggesting that the former might play a key role in stabilization. Environmental stochasticity in the form of coloured noise may therefore be causing a major part of the variation in rain forest dynamics and explain its complexity. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction At the scale of decades to centuries, almost every natural forest ecosystem is subject to perturbation due to climatic variability. Perturbation in this context means a more-or-less strong effect on a forest, but not one so strong as to be called a disturbance (which causes part or whole forest destruction). Perturbations are taken to be irregular in frequency, and often fluctuating in their intensity (Vasseur and McCann, 2007). They form what can be termed ‘environmental noise’ (Bennett and Chorley, 1978). And, being stochastic, perturbations are to be described in terms of the probability of an event occurring in a given interval of time, together perhaps with the mean or range of intensity across several events. Ideally, the dynamics of a forest under such a noisy regime is best described when there are many independent events observed over a long period of time. Attempts to understand the effects of rare ecologically-important perturbations often come, however, from just one or two observed events. These opportunities can provide a basis for a testable hypothesis or a model, despite the high level of unpredictability of the events themselves.

⇑ Corresponding author. Tel.: +41 31 631 8815; fax: +41 31 631 4942. E-mail address: [email protected] (D.M. Newbery). 0378-1127/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2011.07.030

The quantitative estimation of effects of this type of variability on ecosystem dynamics started by considering the role of ‘white’ noise in theoretical models (May, 1974). White noise has a highfrequency of perturbations with no ‘colour’, i.e. no temporal auto-correlation. As temporal autocorrelation increases the colour reddens from pink, through red to brown, indicating ‘events’ more and more strongly in a time-series. Surprisingly, this basic approach has altered little over nearly four decades (Loreau, 2010; Solé and Bascompte, 2006), although in the meantime it has become clear that most forms of environmental perturbation of relevance to ecosystems are moderately to strongly coloured (Halley, 1996, 2007; Steele, 1985; Vasseur and Yodzis, 2004). The majority of textbooks and monographs in ecology do not mention the phenomenon at all, or if they do it is with just a glancing reference. The role of noise remains a highly challenging and neglected area of ecology, both theoretically and empirically (see the preface to May, 2001). It is likely to be of much importance for the understanding of complex ecosystem dynamics (Halley and Inchausti, 2004). Those few attempts to study environmental coloured, or ‘reddened’, noise in ecology to date have been largely concerned with the population dynamics of single animal species, made tractable by their sometimes long time series of data. Whether pink/red/ brown noise increases or decreases population fluctuations

2096

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

appears to depend primarily upon population growth rate, degree of density-dependence and the presence of over- or under-compensation (Cuddington and Yodzis, 1999; Halley and Kunin, 1999; Petchey et al., 1997; Ripa and Lundberg, 1996). Still unclear though is whether the coloured noise of a population’s time series is caused more by environmental noise or intrinsic dynamics of the population itself (Jonzen et al., 2002; Lundberg et al., 2000). Indeed, it is more than likely that both components interact, and it will be difficult to separate them unless an underlying mechanistic process is linked to the perturbation. Whilst comparable examples from plant population ecology are lacking in the literature, a plausible postulate is that coloured noise could be driving population dynamics of tree species in many forests. Species-specific tree physiological processes may be amplifying or dampening the effects, making forest dynamics correspondingly more or less red. One promising approach, we propose, is to work with tree response variables that both underlie variation in population size and are directly affected by important temporal fluctuations (i.e. the noise) in the environment. Relative growth rate in stem size (rgr) is a highly suitable variable for the following reasons. Firstly, recruitment into a population (i.e. into the smallest size class measured) is often strongly positively dependent upon growth in the size class just below the recruitment threshold. Secondly, mortality is to a large degree negatively related to growth, especially among small trees in the understorey where neighbourhood competition operates strongly (e.g. Stoll and Newbery, 2005, for Danum). These two demographic rates together determine changes in population size over time, and collectively changes in community composition. Thirdly, rgr is determined by basic plant physiological processes (Pearcy et al., 1989; Lambers et al., 1998), particularly those involved in responses to light and water availability which vary strongly under climatic variability. Blackman’s (1919) original measure of rgr is commonly interpreted as a product of unit leaf rate (ULR, g cm2 d1) times leaf area ratio (LAR, cm2/g) (Evans, 1972; Hunt, 1978). In the present tree context, girth (mm) would replace dry mass, so that ULR (suitably re-scaled) might be analogously expressed in units of mm m2 year1 and LAR in units m2/m. Rgr is also associated with plant size and ontogeny, which are important determinants of life-history traits. The main aim of the present paper, then, is to explore differences and changes in rgr at a tropical site where there are clear indications from the local climate history record that coloured environmental noise is probably having a major influence. Between 1986 and 2007 at Danum Valley, in Sabah (Malaysia, N.E. Borneo), we have been following how tree species of lowland rain forest react in their dynamics to perturbing droughts (Newbery et al., 1992, 1999). These dry periods come irregularly and exhibit a wide range of intensities, from being quite minor over some days through to being major and involving large water deficits over many weeks (Walsh and Newbery, 1999), with canopy defoliation and some tree deaths (Lingenfelder and Newbery, 2009; Newbery and Lingenfelder, 2009). Many, but not all, past droughts (defined below) have been associated with the El Nino Southern Oscillation (ENSO), and two such moderately strong ones recently affected our two permanent plots, namely those of 1982/83 and 1997/98 (Beaman et al., 1985; Boyd et al., 2006; McPhaden, 1999; Walsh and Newbery, 1999). Scattered in between them, and up to 2007, there have been many shorter and less intense dry periods of seemingly lower importance for the forest (Newbery and Lingenfelder, 2004, 2009). Danum provides then an interesting site at which to measure how such an ecosystem responds to climatic variability, i.e. reddened noise. Using multiple censusses, tree dynamics (mortality, recruitment and growth) before, during and twice after the 1997/98-drought can be compared. Although it is just this one major event that has occurred within the 21-year span of our research, it is also essential to recognize that, at the start of recording in

1986, our forest was probably under some influence of the previous 1982/83 drought. The frequency spectrum of dry periods at Danum is coloured dark red, with b  2 in the inverse power law equation 1/fb (Newbery and Lingenfelder, 2009). This is a defining feature of the forest environment. Reassuringly, the last 20–25 years appear to be fairly typical of the region’s c. 130-year instrumental record in Sabah (Walsh, 1996). An estimated mean interval length of c. 15 year between major ENSO-events indicates that most large overstorey trees (200–250 years old), and understorey ones (150 years) will experience about 15 and 10 events respectively in the course of their lives. A wide range in interval lengths over time is inherent, though (see Walsh, 1996). In terms of both frequency and intensity, ENSO-related events are also highly spatially heterogeneous across Sabah (Walsh and Newbery, 1999), and in neighbouring Sarawak (Nakagawa et al., 2000). The ENSO – as it is recognized today – is evidenced in the last 6 K years of the palaeoclimate record (Markgraf and Diaz, 2000). It is thought to have extended back 130 K years through several interglacial phases (Cane, 2005). So clearly, this climatic noise has been an essential component of the N.E. Bornean (and the wider region’s) environment for a long time, indeed time enough for natural selection to operate and tree species to adapt to it. The strongest event in the near-distant past (<200 years) was in 1877/78 (Kiladis and Diaz, 1986), one that doubtless led to major forest disturbance regionally and at Danum (Newbery et al., 1992; Walsh, 1996). In the broader context of global climate change, earlier models were predicting an increase in the frequency of strong drought (Hulme and Viner, 1998; Timmermann et al., 1999), but this has not been borne out by recent data and advanced analysis (Latif and Keenlyside, 2009). Finally, the idea that ENSOs might form important environmental noise of global significance was recently highlighted by McPhaden (2007). The paper here focuses on relative growth rates of stems of the commoner species of the forest understorey across four periods: P1 (1986–1996), P2a (1996–1999), P2b (1999–2001) and P3 (2001– 2007). Several reasons justify this seemingly restrictive approach. (1) At Danum small trees show the greatest tree species diversity and largest ranges in dynamics variables compared to large ones. (2) They have the clearest spatial distributional patterns in relation to the local topographic gradient (Newbery et al., 1996). (3) There is evidence for physiological differentiation of a drought-tolerant guild (Gibbons and Newbery, 2002). (4) Previous dynamics studies led us towards a facilitation model, whereby understorey species nurse the small trees of overstorey species during periods of drought-stress (Newbery et al., 1999). Accordingly, there is increasing evidence that understorey tree species play an important role in determining the structure and composition of lowland dipterocarp forest at Danum, as they likely do elsewhere in Borneo where drought perturbation prevails. The specific questions to be addressed, therefore, are: (i) How does variation in growth response within species compare to that between species over time? (ii) How, on average, do understorey species respond to drought perturbation, and how do they differ from small trees of overstorey species in this respect? (iii) How fast do the different over- and under-storey species recover? (iv) How can stabilizing reactions be quantified? (v) Is colour of environmental noise important for interpreting forest dynamics at Danum? 2. Climate at the study site The study plots are located within the Danum Valley Conservation Area, Sabah, Malaysia, c. 70 km inland of the NE coast of Borneo, in an area of primary lowland dipterocarp forest (c. 230 m a.s.l.) on gently undulating topography c. 0.8 km from the Field Centre (4°570 4800 N, 117°480 1000 E). Newbery et al. (1992, 1996, 1999) give further details.

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

From 1986 to 2007 (n = 22 years) the mean (±SD) monthly rainfall (per annum) at Danum Valley Field Centre (152 m a.s.l) was 236 ± 37 mm (range 160–295 mm) and the mean monthly temperature 26.87 ± 2.40 °C (range 26.38–27.36) (Fig. 1a; data source DVFC at www.searrp.org). Temperature increased significantly at 0.24 °C/decade (F = 14.5, df = 1,21, P = 0.001; see also Walsh et al., 2011); and so did monthly rainfall by 14.6 mm/decade, but not significantly (F = 1.40, df = 1,21, P = 0.25). This temperature rise is close to that of 0.26 °C/decade (1976–1998), derived by Malhi and Wright (2004) for the tropics globally. The whole study period can be divided (in units of calendar years) into four phases which optimally match both the ENSO event and plot enumerations: 1986–1996 (11 years), which includes all of P1 and part of the first year before the ENSO in P2a (mean temperature = 26.71 ± 0.14 °C, rainfall 231 ± 24 mm); the two ENSO years 1997–98 in P2a (27.11 ± 0.35, 169 ± 13), the two following La Nina years 1999– 2000 in mostly P2b (26.67 ± 0.07, 269 ± 7), and the remaining 2001–2007 (7 years) in the end of P2b and all of P3 (27.10 ± 0.08 °C, 247 ± 32 mm). Thus temperature rose during the ENSO, dropped in La Nina and rose again later: rainfall had the opposite pattern decreasing during the ENSO, increasing (strongly) in La Nina and then falling again later (Fig. 1a). Inter-annual variability in temperature (expressed in terms of the CV of annual means) was similar between the first and last phases (0.52% and 0.30%, respectively, n = 11 and 7), and likewise for rainfall (11.6 and 13.0%). CV of temperature was negatively correlated with date though not significantly so (r = 0.255, df = 20, P = 0.25), whilst that for rainfall was significantly positively correlated (r = 0.510, df = 20, P = 0.015) (Fig. 1b). The two series are well synchronized up until 2004 and then diverge (Fig. 1b); up until 2004 too is there a clear cycle with three peaks (four if the start value is one), and for temperature four troughs. These features have not previously been remarked upon: they reflect components of the reddened noise. Drought intensity per annum was 975 mm for P1 and 1997 mm for P2a (Newbery and Lingenfelder, 2004), where a drought was defined as occurring when the 30-day cumulative sum of rainfall was <100 mm, and intensity as the R{deficit per event  dura-

2097

tion}/interval length (years). Applying the same approach to daily rainfall up to 2007, intensity fell to 239 mm in P2b but rose again to 592 mm in P3. P2b had in fact only one event of 22 days, whilst P3 had 14 events of on average 11 days. Mean deficit in P3 was 12 mm, compared with 14 mm in P1, 30 mm in P2a and 27 mm in P2b. The antecedent rainfall history analysis of Newbery and Lingenfelder (2009) was extended from 2004 to 2010 to show a very similar pattern in negligible rainfall deficit extending from 2001 and 2004 through to at least 2007. The key feature in the time series was this pronounced dry 1998 El Nino (starting in 1997) and its antiperistatic wet La Nina. These constituted the complete ENSO event. Further details of the 1997/98 ENSO are found in Walsh and Newbery (1999), and the rainfall analysis in Newbery and Lingenfelder (2004, 2009).

3. Methods and data set 3.1. Forest plots The two 4-ha plots at Danum were first measured in August 1985–December 1986 and censussed for deaths, recruits and stem growth of all trees P10 cm gbh (girth at breast height; P3.18 cm dbh, or diameter at bh) in November 1995–February 1997, February 2001–February 2002, and most recently in March–October 2007 (January–February 2008 very large trees). These are referred to as the 1986, 1996, 2001 and 2007 full enumerations, respectively. Sixteen 40-m  40-m subplots (totalling 2.56 ha; a stratified random sample across ridges and lower slopes) were censussed for deaths, and stem growth of just the small trees (10–<50 cm gbh) in December 1998–March 1999 (1999 partial enumeration). No recruits were recorded in 1999. Full details of the field methodology and preliminary treatment of the data 1986–2001 are to be found in Newbery et al. (1992, 1999), and in Newbery and Lingenfelder (2004, 2009). Methods used in 2007 very closely followed those in 2001. The mean number of small trees over the five enumerations in the 16 mentioned subplots was 4977 (1944 ha1) with a

Fig. 1. The climate at Danum across all periods of forest measurement, 1986–2007: (a) annual means of monthly mean temperature (closed circles) and sums of rainfall (open circles), and (b) annual coefficients of monthly variation (CV) of these two variables. The closed triangles indicate exactly the median dates of the five plot enumerations, thereby defining the dynamics periods P1, P2a, P2b and P3 (see text). Years are centred on large ticks on the X-axis (labelled), running between the small ticks either side of them.

2098

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

mean basal area of 6.74 m2 ha1 and consisting of 323 tree species (Appendix A). Numbers declined from 5190 to 4778 over the 21 year (7.9%), and basal area from 6.98 to 6.55 m2 ha1 (6.2%) indicating a gradual thinning in the understorey with time. Across the four full enumerations mean density and basal area of all trees (P10 cm gbh) in the two main 4-ha plots were 2141 ha1 and 32.59 m2 ha1 (Lingenfelder and Newbery, 2009; Newbery et al., 1999; and unpubl. data for 2007). The corresponding values for small trees (P10–<50 cm gbh) at this plot scale were 1926 ha1 and 6.91 m2 ha1. As a consequence, largely of the stratified sampling procedure, subplot values for small trees were only slightly less than whole-plot ones but still highly representative of them. Small trees (subplots) constituted 90.7% and 20.7%, respectively of all-trees density and basal area in the whole plots. Quadratic equation fits for logarithms of small-tree frequencies vs. gbh were almost identical for the four main enumerations, indicating an almost-unchanged understorey density structure across periods (D.M. Newbery, unpubl. data) and confirming the constancy of this size class for examining species’ stabilities free of obvious recent disturbance. Gbh is used in this paper, rather than dbh (=gbh/pi), because it is the direct field measurement, gives exact correspondence to whole-cm class limits, and is compatible with our previous publications. 3.2. Growth calculations Only small trees of the 2.56-ha of subplots, for the census periods P1, P2a, P2b and P3 were analysed. The 1997/98 El Nino therefore occurred in P2a. The focus was on change in stem relative growth rate (rgr, mm m1 year1) over time (rgr = {[ln (gbht2)  ln (gbht1)]/(t2  t1)}  1000, where gbht1 and gbht2 are girths (in cm) at times t1 and t2 (in years). With no counts of recruits in 1999, using the 10-cm gbh lower size threshold would have biassed growth rate differences between intervals. This was overcome by considering just the size class 12.5–<50 cm gbh (or 4.0–15.9 cm dbh). Appendix A explains the approach taken, and the sample sizes achieved (76% of the small trees). A ‘valid’ rgr was one where points of measurement both at the start and end of a period were suitable (see Lingenfelder and Newbery 2009, and Newbery and Lingenfelder 2009, for a full explanation). The 48 most abundant species (mean nv of valid rgr-estimates across all periods P15, no period having nv < 12) were selected (Table 1). Arithmetic means of rgr were used: across the 48 species and periods these were highly correlated with geometric means and medians, with very few outliers when graphed against one another (P < 0.001). This indicated a minimum of unusual rgr frequency distributions. An index of ‘reactivity’, rea, was constructed as the difference in a species’ rgr in P2b (rgr2b) and P2a (rgr2a) divided by its average rgr in P1 and P3, mrgr13. This index can be partitioned into: ‘reaction’, rea1, the difference between mrgr13 and rgr2a divided by mrgr13; and ‘recovery’, rea2, the difference between rgr2b and mrgr13 similarly standardized (rea = rea1 + rea2).

rgr 2b  rgr2a mrgr 13  rgr 2a ; rea1 ¼ ; mrgr 13 mrgr 13 rgr 2b  mrgr 13 rea2 ¼ mrgr 13

Payne et al., 2009; see Appendix B). The influence of gbh was thus very small compared with very much larger between-species and between-period differences (Table 1), possibly being of importance for just three out of the 48 species. Removing influences of gbh completely from the within-species rgr data (by fitting models of rgr on gbh; periods either pooled or using P1 as a reference) was not only unnecessary but would have confused differences where the relationship changed across periods. 3.3. Comparison of species’ rates across periods Mean growth rate was compared between periods, in all six pair-wise successive combinations (P1 with P2a, P1 with P2b. . .P2b with P3), for each species in turn. Half of the mean number of trees with valid growth rates were selected at random (without replacement) from the population in the first period to give a sample 1. These individuals – when surviving with valid rates – were flagged in the population of the second period, and a sample 2 found by randomly selecting from the non-flagged individuals. That no tree was used in both samples ensured statistical independence in this respect. The difference in rgr (rgr1  rgr2) was calculated. The process was repeated 5000 times using a FORTRAN program and the mean difference, with 5%, 2% and 1% confidence limits (defined by corresponding quantiles of the simulated values), determined. A standard randomization test would then have simply asked whether the confidence limits included the zero, and if that were not the case the null hypothesis of no difference between periods rejected. However, if rgr was sometimes negatively related to gbh, as was demonstrated above, and survivors generally increased in gbh over time because they had grown, then flagging and excluding trees in population 2 when deriving sample 2 would have removed on average a set of larger-than-average trees. Because these sample-2 trees tended to have slightly lower rgr-values than sample-1 trees this selection procedure would then have biassed (i.e. decreased) the simulated differences in rgr between samples. Over time some trees in population 1 will have died by the succeeding period (often having had lower rgr-values prior to death), other larger-gbh ones close to 50 cm will have moved up and out of the small-tree size class (also tending to have slightly lower rgrvalues than average), whilst new recruits (just >10 cm gbh) into population 2 will have generally higher-than-average rgr-values. The net result is that differences (dsim) were much smaller than those directly found by comparing the means of periods as complete non-sampled populations (dreal) especially when comparing P1 with later periods. The null hypothesis was therefore adjusted, separately for each species and pair of periods considered, to 0  (dreal  dsim). To reach significance the new H0-value must lie outside the confidence limits. Combining the effects of gbh on rgr within periods with variation in mean gbh across periods, however, suggested that rgr-values can be compared across periods with very low risk of any important confounding effect due to shifts in gbh (see Appendix B). 3.4. Over-understorey index

rea ¼

Reactivity, in a much more detailed form than used here, was already introduced into ecology by Neubert and Caswell (1997) and later extended by Neubert et al. (2009). Differences in rgr between periods may have been confounded by slight shifts in gbh with time. Rarely though, if indeed at all, was rgr (negatively) weakly related to gbh within species (gbh range 12.5–<50 cm; GLM fits with gamma-distributed errors,

A revised over-understorey (OU)-index for species (Newbery et al., 1992) was constructed. At the whole-plots scale, for each species, numbers of all trees (P10 cm gbh) and numbers of those P30 cm gbh were found, with their corresponding sums of basal areas. From them density (den-) and basal area (ba-) ratios were calculated (ngbhP30/ngbhP10 and bagbhP30/bagbhP10). The relationship ln (1  ba-ratio) vs. den-ratio was linear in all cases (Pearson’s r between 0.892 and 0.909, P  0.001). Scores of the first axis from a PCA of these two variables for the 100 most abundant species (in terms of density of all stems) at each enumeration were

2099

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

Table 1 Summary data for the 48 tree species (12.5–<50-cm gbh) from 2.56 ha of the two 4-ha plots at Danum. Abb, the codes used in figures in main text; nv, mean number of trees with valid growth rates across periods; rgr, the mean relative stem growth rates (mm m–1 year–1) for the periods P1, P2a, P2b and P3; OU, the over-understorey index for each species (0–100 scale, without units); grp, species groups based on OU:1 (u), understorey; 2 (i), intermediate storey; 3 (o), overstorey; gbh, mean gbh across periods (cm). Significant differences in rgr between periods p and q at level s are shown as pqs (s: 1, P 6 0.05; 2, P 6 0.01; 3, P 6 0.001). At the base of table are summaries of mean rates for the 48 and all species, and for species in the three storeys. Species

abb

nv

rgr

OU

gbh

grp

9.14 7.44 10.80 5.42 10.17 10.49 6.19 7.58 13.97 9.20 6.68 3.75 3.70 8.01 17.58 15.34 12.29 14.06 18.45 3.31 11.88 7.21 14.10 28.19 7.11 14.09 8.00 8.27 0.94 8.72 31.35 11.51 10.48 7.01 12.66 6.92 11.73 10.15 13.06 11.33 5.81 15.39 8.90 8.27 5.12 5.29 8.31 5.57

37.9 22.0 54.1 0.3 6.8 40.8 60.4 17.8 49.0 3.1 26.1 15.6 32.2 9.8 50.2 24.5 4.0 42.3 31.1 10.6 22.0 5.1 57.1 16.5 27.7 18.3 32.1 9.1 6.1 19.4 27.9 10.4 0.8 65.1 63.0 12.0 30.2 20.0 44.0 14.9 18.1 39.2 65.8 37.5 23.2 63.0 57.7 28.7

22.7 19.2 27.1 16.0 19.3 28.7 24.1 19.0 27.1 18.8 23.9 19.7 22.8 20.7 30.3 22.4 19.4 18.9 22.2 18.1 24.8 18.1 27.3 23.6 25.5 23.4 20.6 18.2 17.3 19.0 24.1 20.2 17.5 27.2 28.1 21.3 26.0 23.9 22.9 21.9 23.1 25.9 26.7 24.6 23.1 26.7 25.3 21.3

2 1 3 1 1 3 3 1 3 1 2 1 2 1 3 2 1 2 2 1 2 1 3 2 2 2 2 1 1 1 2 1 1 3 3 1 2 2 2 1 2 2 3 2 2 3 3 2

12.96 (1.03)

10.23 (0.81)

28.6 (2.8)

22.70 (0.5)

7.98 8.88

13.54 13.98

10.55 11.34

21.0 32.3f

21.81 21.81g

6.88 11.18 13.75

5.31 10.04 13.40

9.26 15.03 14.89

7.80 12.16 10.48

9.85 28.97 56.92

19.04 23.29 27.16

7.48 11.59 13.47

5.98 10.13 11.65

12.44 15.20 14.63

9.56 12.38 11.00

9.41 28.32 54.68

19.78 23.26 27.40

P1

P2a

P2b

P3

10.75 10.96 12.96 1.43 11.76 7.43 7.02 7.16 11.58 7.53 7.10 3.54 5.18 4.69 22.21 16.41 9.55 12.01 23.03 3.02 9.21 9.99 17.19 16.96 17.32 16.41 10.11 4.31 2.85 8.95 11.35 8.37 7.78 14.13 16.10 4.44 9.14 8.32 16.74 10.73 5.48 16.72 17.91 4.03 4.36 15.50 9.19 2.95

7.21 5.15 5.25 0.72 6.89 5.20 3.82 8.67 15.68 4.50 8.14 0.35 4.95 4.43 15.17 13.45 6.59 12.20 23.83 2.03 8.90 10.12 18.79 16.07 9.63 17.73 9.76 3.82 2.86 10.01 6.02 7.22 4.19 14.81 13.23 2.84 13.34 8.17 16.26 9.87 1.88 7.22 23.25 9.17 4.88 15.48 16.71 1.96

16.51 9.76 10.64 4.67 13.19 11.53 9.42 6.82 19.76 12.17 8.70 4.00 3.11 14.25 21.60 23.57 11.36 11.74 28.38 1.24 13.01 9.22 23.16 25.99 10.62 25.65 12.03 8.97 2.99 13.67 31.44 15.43 11.58 10.30 16.55 6.06 12.88 20.00 24.11 12.11 4.35 12.33 17.23 7.70 5.66 9.41 14.19 2.90

10.25 (0.77)

9.13 (0.77)

– –

9.53 10.74

Mean of species as – 17 u-spp.c – 20 i-spp.d – 11 o-spp.e

73.8 32.1 26.3

Mean of all trees of – 17 u-spp. – 20 i-spp. – 11 o-spp.

– – –

Aglaia sylvestris Alangium javanicum Aporosa falcifera Antidesma neurocarpum Ardisia sanguinolenta Baccaurea tetandra Barringtonia lanceolata Buchanania insignis Chisocheton sarawakanus Cleistanthus contractus Dacryodes rostrata Dehaasia gigantocarpa Diospyros elliptifolia Dimorphocalyx muricatus Dysoxylum cyrtobotryum Dysoxylum rigidum Fordia spendidissima Gonystylus keithii Hopea nervosa Hydnocarpus borneensis Hyndocarpus polypetalus Knema latericia Lithocarpus nieuwenhuisii Litsea caulocarpa Litsea ochracea Lophopetalum beccarianum Madhuca korthalsii Magnolia candollei* Magnolia gigantifolia Mallotus penangensis Mallotus stipularis Mallotus wrayi Maschalocaorymbus corymbosus Parashorea malaanonan Pentace laxiflora Polyalthia cauliflora Polyalthia congesta Polyalthia rumphii Polyalthia sumatrana Polyalthia xanthopetala Reinwardtiodendron humile Shorea fallax Shorea pauciflora Syzygium chrysanthum Syzygium elopurae Syzygium lineata Syzygium tawaense Xanthophyllum vitellinum Mean of 48 species’ means and (SE) Mean of all trees of – 48 spp.a – all spp.b

Asi Aj Af An Asa Bt Bl Bi Cs Cc Dr Dg De Dm Dc Do Fs Gk Hn Hb Hp Kl Ln Lc Lo Lb Mk Mca Mg Mp Ms Mw Mco Pm Pl Pca Pco Pr Ps Px Rh Sf Sp Sc Se Sl St Xv

20 17 51 15 95 46 22 29 26 71 40 28 16 182 31 19 89 27 16 21 30 26 20 53 39 45 76 22 18 40 20 441 50 16 24 83 20 42 32 28 29 54 19 15 26 19 17 26 45.5 (9.4)

Differences in rgr

123,232,243 142,242 123,233 231

233,243

133,143,233,243,343

232,243

341 143,243 122,143 341 231 131 123,133,143,233,243 133,141,233,243,343 233,242

141,233243 131,233

121 123,241 241

143,241

a,b

Total n trees of 48 and all species = 2184 and 3028. Total n trees of u-, i- and o-spp. = 1254, 641 and 289. Average of first 100 ranked spp. (for which OU-index was found). g Mean of four period means (defined by mean gbh start and end). * var. singapurensis.

c,d,e f

re-scaled (and inverted) to give indices ranging from 0 to 100, so that increasing den- and ba-ratios meant a higher OU-index. (PCA

axes 1 accounted for 92.9–95.5% of variance). The four lists were combined and the mean index for each species was calculated.

2100

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

4. Results 4.1. Variation in growth rates within and between species The 48-species showed a changing growth response profile across the four periods when ranked according to their mean rgrvalues (Fig. 2). In P1 the decline from highest to lowest was close to linear, becoming similarly more concave in P2a and P2b (except perhaps for the 10–12 species in the tail), and then even more concave, at least in the first half of the series, in P3 (Fig. 2). Judged by the bootstrapped 95% confidence limits, rates for most species were more variable in P2a and P2b than in P1 and P3 – possibly, in part, due to the differing interval lengths. Whilst many species evidently benefited from the perturbation, in terms of enhanced growth rates in P2b, most returned to values matching the majority of species in P3. As exceptions, the two first ranked which re-

tained very high rates (from P2b to P3). Whilst mean rgr was poorly and insignificantly correlated with the number of cases (ln (nv)) within each period (r = 0.215 to 0.100, P = 0.14–0.59), the 95% confidence interval (difference between upper and lower limits) was strongly negatively so (r = 0.529 to 0.353, P < 0.001–0.014). This was largely a reflection of population size differences. Non-overlap of confidence limits (Fig. 2) is not a fully reliable way to infer significant differences between species, but it offers a general guide. For instance, roughly the first and last thirds of the ranked rgr-values do not overlap in P1 and P2a, but in P2b and P3 they do. The patterns of changing rgr across the four periods for the 48 species do, at first sight, seem highly idiosyncratic (Table 1). The adjusted pair-wise tests showed the following numbers of species out of the 48 as significant (P 6 0.05): P1–P2a, 6 (all ve differences); P1–P2b, 6 (all +ve); P1–P3, 8 (2 ve, 6 +ve); P2a–P2b, 12

(a) Period 1

(b) Period 2a

40

40

30

30

rgr

50

rgr

50

10

10

0

0 Hn Sp Ln Lb St Ps Lc Cs Sl Dc Pm Do Pco Pl Gk Kle Mp Px Mk Lo Sc Hp Bi Pr Dr Sf Mw Asi Asa Fs Ms Af Bt Aj De Se Cc Dm Mco Bl Mca Mg Pca Hb Xv Rh An Dg

20

Hn Dc Sp Lo Ln Lc Ps Sf Lb Do Pl Sl Pm Af Gk Asa Cs Ms Aj Asi Px Mk Kle Fs Hp Pco St Mp Mw Pr Mco Cc Bt Bi Dr Bl Rh De Dm Pca Se Mca Sc Dg Hb Xv Mg An

20

species

species

(c) Period 2b

(d) Period 3

40

40

30

30

rgr

50

rgr

50

10

10

0

0 Ms Lc Hn Dc Do Sf Gk Lb Ln Cs Ps Pl Fs Hp Pco Mw Px Af Mco Bt Asa Pr Cc Asi Sp Mp Mca St Sc Dm Mk Bi Aj Kle Lo Pm Pca Dr Bl Rh Xv An Sl Se Dg De Hb Mg

20

Ms Hn Lc Lb Ps Do Ln Dc Pr Cs Sp Asi Pl Mw Dm St Mp Asa Hp Pco Sf Cc Px Mk Gk Mco Bt Fs Lo Af Pm Aj Bl Sl Kle Mca Dr Sc Bi Pca Se An Rh Dg De Mg Xv Hb

20

species

species

Fig. 2. Mean relative growth rates of stem gbh (rgr, mm m1 year1), ranked highest to lowest, of small trees (12.5–<50 cm gbh) of the 48 most abundant species (in that size class) in 2.56 ha of the two 4-ha permanent plots at Danum combined, for the four census periods (a) P1, 1986–96; (b) P2a, 1996–1999; (c) P2b, 1999–2001; and (d) P3, 2001– 2007. The error bars are 95% confidence limits obtained, using a bootstrapping procedure, as were the means. Species’ codes are explained in Table 1. Graphics in this paper were made with the program R (RDCT, 2010).

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

(all +ve); P2a–P3, 14 (1 ve, 13 +ve); and P2b–P3, 4 (all –ve). Of all of 48 significant cases, 29 (60%) were species ranked 1–12 (top 25%) in abundance. Mean stem rgr of the 48 most abundant species in P1 decreased in P2a, rose much higher in P2b than P1, and returned to close to the P1-value in P3 (Table 1). A similar set of changes was seen

2101

using individuals of these species, except that mean rates were lower in P1 and P2a and higher in P2b and P3. This last result indicated how skew within the former and latter two periods decreased and increased respectively. The average 95% confidence interval changed with time also, from on average 7.36 mm m1 year1 in P1, rising to 8.93 in P2a and 11.73 in P2b, then falling

a

b

Fig. 3. Mean relative growth rates of stem gbh (rgr, mm m1 year1), of the 48 species of small tree (12.5–<50 cm gbh) at Danum plotted against (a) their mean gbh (in that size class), and (b) their over-understorey (OU)-index (based on trees P10 cm gbh – see text), for the census periods (panels): P1, 1986–96; P2a, 1996–1999; P2b, 1999–2001; and P3, 2001–2007. The green dashed lines are simple linear regression fits to illustrate trends. Species’ codes are explained in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2102

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

Fig. 4. Over-understorey (OU)-index (based on trees P10 cm gbh) of those 48 species most abundant as small trees (12.5–<50 cm gbh) at Danum plotted against mean gbh (in that size class) for the four census periods (panels): P1, 1986–96; P2a, 1996–1999; P2b, 1999–2001; and P3, 2001–2007. Species’ codes are explained in Table 1. Points: red, green and blue closed circles indicate under-, mid-, and over-storey classes, respectively.

back (towards the P1-value) to 8.55 mm m1 year1 in P3; the changes being reflected in both negative and positive increases and decreases in skew within species’ rates. Although confidence interval was always strongly significantly positively correlated with mean rgr within each period (r = 0.762–0.828, df = 46, P < 0.001), the overall mean fell in P2a with increasing interval size. Yet when it increased even higher than P1 in P2b the interval increased further. This suggests that within species the perturbation in P2a increased differences between individuals, and after that in P2b this variation was further increased. 4.2. Growth rate, size and storey across species Rgr was strongly positively correlated with mean gbh across species (Fig. 3a), more strongly for P1 and P2a (r = 0.553 and 0.443, df = 46; P < 0.001 and 0.002) than P2b and P3 (r = 0.350 and 0.390, P = 0.015 and 0.006, respectively). Most noticeable was a group of six species in P2b with higher-than-average rgr-values at intermediate gbh-values. Of these, Hopea nervosa, Mallotus stipularis, Litsea caulocarpa, Lophopetalum beccarianum, Polyalthia sumatrana and Dysoxylum rigidum, the first had high rgr in all periods whilst second and third retained high rates in P3 (Fig. 3a). Species’ confidence intervals of rgr graphed against mean gbh had similar trends. The correlation between rgr- and OU-index changed with period (Fig. 3b): in P1 and P2a it was significantly positive (r = 0.506 and 0.557, respectively; P < 0.001) but in P2b and P3 it was weaker (r = 0.244 and 0.114, P = 0.09 and 0.44, respectively). The rgr-values varied much less across the range of the index in P3 than in P1 and P2a: in P2b they had a greater variation around mid-range values

of the index. Independent of rgr, the index was strongly correlated with mean gbh in the 12.5–<50-cm size class across species (r = 0.758–0.799, df = 46, P < 0.001; Fig. 4). First axis (species’) scores from PCAs of the index and gbh in each of the four periods were classified (agglomerative centroidsorting based on Euclidean distance) to give three OU-groups labelled as under-, intermediate- and over-storey species (Table 1). Taken en suite, mean rgr and its interval, and gbh and OU-index, were all generally positively inter-correlated, and the first axis of PCA of them accounted for 70%, 70%, 63% and 62% of the variance in P1, P2a, P2b and P3, respectively. If, however, gbh is partialled out, the correlations (r⁄) between rgr and OU-index in P1, P2a, P2b and P3 became 0.127, 0.378, 0.052 and 0.344 (P = 0.395, 0.008, 0.727 and 0.018, respectively), i.e. the positive correlation with OU-index in P1 was lost, in P2a it remained positive (still significant, though less strongly), in P2b it stayed weak yet more negative, and – surprisingly – in P3 it changed to becoming significantly negative. With time the relative response across storeys reversed: index per se seemed to operate in P2a and P3 mostly. The rgr in P3 was most strongly correlated with rgr in P2b across the 48 spp. (r = 0.831, df = 46, P < 0.001), least with rgr in P2a (r = 0.437, P = 0.002) and intermediate with rgr in P1 (r = 0.597, P < 0.001). A graph of rgr in P3 versus P1, essentially a test of stability of rates over the periods (not shown) had six outliers, two very high (L. caulocarpa and M. stipularis) and four moderately low (Litsea ochracea, Parashorea malaanonan, Syzygium lineata and Shorea pauciflora). The former species were growing much faster, the latter much slower, in P3 than P1. Removing these points led to a stronger correlation around a 1:1 line (r = 0.880,

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

2103

Fig. 5. Relative change in mean relative growth rates of stem gbh (rgr, mm m1 year1), of the 48 species of small tree (12.5–<50 cm gbh) at Danum plotted against overunderstorey (OU)-index (based on trees P10 cm gbh), for the census periods (panels): P2a, 1996–1999; P2b, 1999–2001 and P3, 2001–2007 relative to P1, 1986–1996; and P3 relative to P2b. The green dashed lines are simple linear regression fits to illustrate trends: red lines represent no change between periods. Species’ codes are explained in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

P < 0.001). Most species therefore returned to earlier rates after the perturbation in P2a–P2b. Two of the four slowed species were overstorey dipterocarps. Indeed, the mean growth rate rgr2a–2b (11.05 mm m1 year1) was only slightly higher than the mean growth rate rgr1–3 (10.24 mm m1 year1). Plus that these two rates were strongly correlated (r = 0.876, P < 0.001) indicated an overall stabilization of the 48 species. 4.3. Change in rgr and reactivity Relative growth rates of the 48 species in P2a, P2b and P3, when expressed as simple proportions of the corresponding rates in P1 (the reference period before perturbation), were marginally positively correlated in P2a (r = 0.277, P = 0.056) and significantly negatively correlated in P2b and P3 (r = 0.300, P = 0.038 and r = 0.388, P = 0.007, respectively), with the OU-index (Fig. 5). A point with an index value of 50 (60) and proportional change in rgr of 1.0 appeared to act like a pivot for the fitted linear regression lines. Understorey species (OU-index  10) had proportions which swung strongly from c. 0.5 in P2a, up to c. 2.0 in P2b and then back to c. 1.5 in P3 (Fig. 5), whilst overstorey species remained clustered around the pivot and changed little with time. Thus, whilst the understorey species reduced their rgr-values most in P2a, compared with the lack of change in the overstorey ones, they also increased most in P2b, and retained more of the response in P3 than the overstorey ones. From P2b to P3 the relative changes were weakly correlated with the OU-index (r = 0.234, P = 0.11). When gbh in P1 was partialled out the correlations between the four rgr-changes and OU-index all became insignificant (P = 0.12–0.54).

Across the 48 species, rea was significantly negatively correlated with OU-index (r = 0.425, df = 46, P = 0.003) and so was rea1 (r = 0.445, P = 0.002), but not rea2 (r = 0.076, P = 0.61) (Fig. 6). This indicates that the part of reactivity most strongly associated with OU-index was the reaction. The pivotal effect was again shown with rea, i.e. its value was high for understorey, but near zero for overstorey, species. There was no trade-off between rea1 and rea2 as they were uncorrelated (r = 0.182, df = 46, P = 0.22): a further indication of the idiosyncratic pattern of responses. 4.4. Growth rate in relation to mortality and recruitment Over the four periods (P1, P2a, P2b and P3) uncorrected annualized mortality rates (ma) were 1.50%, 2.10%, 2.14% and 1.70%/year for small trees (10–<50 cm gbh). Applying period-specific factors to correct for differences in period length to a 5-year basis (1.109, 0.834, 0.849 and 1.039; Newbery and Lingenfelder, 2004) these rates became 1.71%, 1.75%, 1.81% and 1.77%/year respectively. Annualized recruitment rates (ra), with recruits referenced to number of trees at start of period were 1.50%, 1.09%, 2.70% and 1.94%/year. Thus whilst ma showed very little variation with time, ra did, closely parallelling changes in small-tree rgr. Using the 48 species’ ma (uncorrected), ra and rgr rates, repeatedmeasures analysis of variance revealed no significant effect of period or OU-group (P > 0.05) for ma, of only period for ra (F = 7.56, df = 3, 135; P = 0.001), yet both period (F = 11.70, df = 3, 135; P < 0.001) and group (F = 6.76, df = 2, 45; P = 0.003) – plus a marginally significant period  group interaction (F = 2.27, df = 6,

2104

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

Fig. 6. Species reactivity, in terms of differences in relative growth rates of stem gbh (rgr, mm m1 year1) between periods, of 48 most abundant species as small tree (12.5– <50 cm gbh) at Danum plotted against their over-understorey (OU)-index (based on trees P10 cm gbh), for (panels): overall (rea), and partitioned into reaction (rea_1) and recovery (rea_2). The green dashed lines are simple linear regression fits to illustrate trends; red lines represent no change between periods. Species’ codes are explained in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

135; P = 0.062) – for rgr. The means for rgr appear at the end of Table 1. Across the 48 species and four periods ma and ra were furthermore very weakly correlated with the OU-index (P = 0.14– 0.82). These results re-emphasize that rgr was far more directly responsive to perturbation than ma. In P1 and P3, ma- and ra-values across the 48 selected species were approximately normally distributed so linear regression could be used, whilst in P2a and P2b, where many zeros of both variables occurred, logistic regression was more appropriate (non-zero rates being set to 1). In none of the periods was ma dependent upon rgr (P = 0.63–0.86), whilst ra depended significantly positively upon rgr in P1 (t = 3.22, P = 0.002) and P3 (t = 2.42, P = 0.019) – although not for P2a (z = 1.46, oddsratio = 1.09, P = 0.15) and not for P2b (z = 0.26, odds-ratio = 1.01, P = 0.79). In addition, ra in P2a was significantly dependent on rgr in P1 (z = 2.33, odds ratio = 1.18, P = 0.020), ra in P3 upon rgr in P2b (t = 2.103, P = 0.041), although not ra in P2b on rgr in P2a (z = 1.13, odds ratio = 0.94, P = 0.26). Mortality was again unrelated to rgr in small trees in all cases (P = 0.21–0.88). The strongest evidence for the inter-relationship between ra and rgr was therefore found in P1; and after that it disappeared only to recover in a lagged form in P3. Four, 12, 14 and five of the 48 species had zero ma-rates in P1–P3, respectively, and correspondingly 4, 18, 12 and 7 zero ra-rates. These zero rates were mostly associated with small population sizes; the 3- to 4-fold increase in their numbers in P2a and P2b over P1 and P3 is in part a reflection of the shorter, respective longer, interval lengths. As a further indication of no direct link existing between rgr and tree population dynamics (i.e. ma and ra together), neither population change (ln (n07/n86)) nor the CV of population size over the five

enumerations (n86. . .n07) were significantly correlated with mean rgr over the four periods (r = 0.246, P = 0.092; and r = 0.143, P = 0.33, respectively). The average (± SE) population change across species (non-transformed; R = 1 means no change) was 0.979 ± 0.012 (min = 0.752, max = 1.171), i.e. very little change overall in this size class over 21 years with those species that increased being almost completely compensated for by those that decreased. 5. Discussion 5.1. Stabilization of tree growth in the understorey In terms of stem relative growth rate a clear general trend in response to the ENSO perturbation was shown. Almost half of the 48 species examined had significant differences in rgr between periods. Tree species differed very considerably between one another, in form, timing and strength of their reaction and recovery to the perturbation, showing a plurality of reactive responses. A further notable feature of these Danum small-tree rgr-values was their very high within-species variability. In fact, relatively few species, perhaps a third, differed significantly from one another within any one period; and the range in species’ mean rgr was little more than twice that of the average confidence interval. So whilst there were some clear species-specific differences in rgr, there was also much additional variation left to be explained by the many other abiotic (e.g. topographic) and biotic factors, as well as other components of the environmental noise besides the highlighted ENSO event. There are notably very few other reports of actual tree stem rgr in the tropics (e.g. Clark and Clark, 1999; Condit et al., 2006; Feeley et al., 2007).

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

The changes in total density and basal area of small trees were relatively small across the five enumerations indicating a structurally near-constant understorey environment: the gradual thinning was to be expected because the forest at Danum has recently been continuing in a late seral stage (Newbery et al., 1992, 1999). Free of other major disturbances makes this size class highly suitable for testing ideas about relative species dynamics affected by the ENSO perturbation, with the confidence also that the subplots were well representative of the main plots. That mean gbh shifted very little with time, despite many small trees dying and recruiting, meant a very small degree of potential bias in interpreting rgrs over time. Interactions with the climatic environment and neighbouring competitors are both expected to be strong for small trees in the understorey – where overstorey species struggle up into the main canopy and understorey species try to maintain positions under that canopy – and this leads to various outcomes for forest composition and ecosystem properties. This is not to deny that seedlingto-sapling processes and the dynamics of very small trees (2– 10 cm gbh) may also be important, as will be the dynamics of the larger trees (>50 cm gbh) at spatial scales larger than the Danum plots. Nevertheless, we maintain that the much-understudied ‘middle’ of the forest holds promise of interesting insights into whole forest dynamics via perturbation-stability analysis. In this context, the forest at Danum appears to be quite stable and resilient to a regime of moderately-strong drought perturbation, at least in terms of rgr of small trees (and noting too that responses of all and small trees through mortality were very small: Newbery and Lingenfelder, 2004, 2009). Mean rgr declined during the dry P2a, likely mainly due to the temporary water stress (Gibbons and Newbery, 2002; Walsh and Newbery, 1999). It then increased again, or even over-compensated, in P2b, due most likely to temporarily raised light levels within the understorey resulting from defoliation and small-branch thinning in the canopy. Finally, the trees returned, in the much wetter P3, to having rgr-values close to those in P1. A majority of species followed this oscillating pattern of response although there were several exceptions. An important insight was that recovery and stabilization was largely over by the end of P2b, and in P3 little more changed by way of species differences and rgr-ordering. Period P1 can be interpreted then as a period of closed-forest conditions, with strong neighbourhood competition occurring in the understorey (Newbery et al., 1992, 1999), the structure becoming more open during and after the ENSO-perturbation (Newbery and Lingenfelder, 2004), and followed by a regaining of that closed status in P3. This means, by and large, that the forest stabilized in 3(–4) years after the ENSO event. 5.2. Environmental stochasticity and other changes This ENSO-centric interpretation rests upon the argument that forest in P3 was equivalent to that in P1, i.e. for the purposes of a stability analysis the forest was supposed to return to a state close to that at the start (before perturbation). If the pattern of response to the 1983 event was similar to the 1998 one, trees at the start of enumeration in 1986 (3 years after 1983) had most likely passed through a recovery phase (similar to P2b): i.e. in 1986 they would have been almost at the point they were in 2001. This would support the notion that indeed the 10-year P1 was a suitable lowdrought-intensity reference period upon which to judge the degree of stabilization (as we have done in this paper). Even so, two other considerations must be made. First, there was evidently a mediumterm 0.5 °C increase in mean temperature (with two underlying decadal cycles), and a c. 15-mm rise in monthly rainfall over the 21 years studied; and second, other anomalies in the climate record, such as the La Nina reflection from 1999 and the higher temperatures in 2004–06. These made P3 slightly warmer and wetter

2105

than P1. Thus it was impossible for the forest to grow again under exactly the same conditions, although the formula for rea did, to some extent, detrend these other components of response by using the means of rates in P1 and P3. Indeed, the plurality of species’ responses may not have been only due to intrinsic physiological and population biology differences between species with respect to the ENSO in 1998 but a mixed response to that event and other features of the climatic noise. Measuring the response of species-rich tree community to a stochastic event that occasionally perturbs a forest ecosystem is problematic in other ways, especially in remote tropical locations like Danum. ENSO-events come once or twice every 20 years (Walsh, 1996), and reliable forewarning is possible perhaps only 3–6 mo beforehand (Chen et al., 2004). Censussing of the 4-ha plots at Danum, including identification of recruits of the better known species in the field (the others by matching in the herbarium later), requires 8–10 mo by a three-person team, and more frequent entry than once every (3–)5 years is impracticable and anyway causes too much disturbance. The 1998-drought occurred for only a few weeks within the c. 2.5-years P2a, and it is assumed that up until that event the forest was otherwise unaffected and had a dynamics similar to that in P1 (Lingenfelder and Newbery, 2009). A longer P2a would have diluted the drought’s effect on tree rgr with time, and therefore decreased its detection. Whether an event occurs towards the start or end (as in P2a) of a period is important because the former would include more recovery time than the latter. If P2 and P3 had been taken together (1996–2007), for instance, the drought effect would have been barely detectable; likewise, although with an opposite effect, if recovery after 1983 had been longer (say 5 years) the overlapping effect into P1 would have been similarly diluted over the 10 years. Taking all these considerations together, the dates and interval lengths (1986–2007: 10.0, 2.4, 2.6 and 7.0 years) appear to have been suitable for studying response to the ENSO perturbation at Danum. Given the evidently idiosyncratic dynamics of the Danum tree species, the minimum sample sizes needed for statistical analysis, spatial heterogeneity within plots, and the definite importance of smaller trees that we have shown, annual recording of large trees of a few abundant species – as is recommended by Clark and Clark (2011) – would not only have led to results that were unrepresentative of the 8 ha of plots but missed testing of our hypothesis. To shorten measurement intervals so much would have meant that growth was estimated with far less accuracy, particularly for the small trees, because gbh increments (using tapes or callipers) would be so much smaller and closer to zero (see Lingenfelder and Newbery, 2009 for discussions on methodology here). Clark and Clark (2011) argue that multi-annual census approaches cannot reliably track the effects of climate change and only annual records can reveal unusual growth responses. The former may apparently detract from, or even bias, detection of continuous tree growth rate responses to variables such as mean temperature. With limited field resources there must be a trade-off between the two approaches. At Danum unusual years, aside from the ENSO/La Nina ones (1997–2000), appear not to have affected too much our stability analysis because the CVs for temperature in P1 and P3 were similar and small, and for rainfall even though larger they were more similar. This suggests that the integrated estimates of rgr over 10 and 6 years, respectively were near to the average rates and are indeed comparable. Shifts in rgr across periods (for all trees, means of species, and many species individually; Table 1) were very much greater than any background trend that might have been longer-term temperature- and rainfall-related. Being completely free of any chance confounding effect is impossible, however. At BCI, Panama, a recent analysis of 25 years of change in forest composition (1981–2005) showed a shift towards more drought-tolerant species early on,

2106

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107

which was likely a response to a prior regional drying trend and the ENSO of 1982/83 within the first census interval (Feeley et al., 2011). It was again difficult to disentangle a later shift back towards more humid conditions as a recovery/stabilization/successional process from the background global trend in temperature and raised atmospheric CO2 concentration. At Danum, furthermore, whilst species’ rgr in the understorey might possibly have increased slightly, due to a small temperature increase over the 21 years (this study and c.f. Malhi and Wright, 2004), this would have been (partly) offset by the demonstrated ontological drift – the small decrease in rgr with overall increasing mean gbh within species (and decreasing stem density). In addition, raised neighbourhood competition for light, expected in a wetter more closed forest in P3 (Stoll and Newbery, 2005), would have played a role. To conclude, it is reasonable to argue that, in the medium-term, tree species at Danum (and perhaps BCI) were predominantly responding to the ENSO driver though other interacting and underlying factors were operating too (in different ways at either site). 5.3. Importance of the understorey in forest dynamics If the general response curve constructed for the 1998-drought can be accepted with its assumptions, it is unlikely that an increased frequency of moderate-strong ENSO’s in the region will endanger the forest at Danum. If events were to come down to as close as 5 years apart, the forest would have time to recover. We have shown elsewhere that the ENSO event in 1998 led to only a slight temporary increase in small- and large-tree mortality at Danum (Newbery and Lingenfelder, 2004, 2009), and that recruitment closely tracked rgr (Newbery et al., 1999). Faster-growing trees around 10.0–12.5 cm gbh tended to recruit more frequently than slower-growing ones. The effect of dry periods is similar to that of release that foresters employ to encourage pole growth in stands, except that in this natural situation at Danum the trees suffered slowed growth before they recovered. This stress on the understorey trees and then their release allows for the more or less resistant species to respond quite differently. It leads to the many varied trajectories in growth under perturbation – the plurality already noted, but no trade-off between species’ response and recovery. If droughts were to greatly intensify (perhaps an event like that in 1878 happening again), then the disturbed forest might react much more drastically in the way Walsh (1996) has proposed for Sabah and Slik (2004) has shown in eastern Kalimantan, with large open areas and succession occurring. The role of the understorey with regard to environmental stochasticity has some implications for forest management too. In harvesting operations the understorey, made up of mostly so-called ‘unproductive species’, is often largely destroyed. Therefore, a major component of the ecosystem, and its mechanism for recovery and medium-term stability, is lost. Opening up of canopy-level gaps is broadly similar to thinning of the canopy that occurs during ENSO events, and under these conditions it is these more drought-tolerant understorey species that can enable recovery of the main canopy. Change in rgr (with respect to P1) and rgr-reactivity (rea) were all significantly correlated with the OU-index. The largest changes over time were experienced by species with the lowest index values, i.e. species predominantly true understorey as a guild, compared with those with higher index values, the overstorey (canopy species). This was despite the fact that the former included most of the smallest stems. For understorey species relative change in rgr decreased more between P1 and P2a, and increased more between P1 and P2b, than for overstorey species. Had only small trees of overstorey species been studied very little interesting change would have been detected for them and the whole forest. Gbh and the OU-index are closely interrelated since species with pre-

dominantly large stems overall tended to have both higher indices and mean gbhs among the small trees. Correspondingly, the reactivity (rea, based on the difference between rgr in P2a and P2b) was large for understorey species and near zero at an OU-index of 0.6 (near the top of the range for the data). Perturbation was affecting the stems of understorey species far more than the small stems of the overstorey ones; or conversely stated the overstorey species having mainly the larger gbhs, and hence being taller, were already in better lighted conditions so that canopy thinning affected them much less than the understorey. Slowing of growth during the dry period does not necessarily mean increased mortality, especially if the tolerant species can recover by growing (even) faster afterwards. The new analysis not only lends support to the hypothesis but suggests a degree of light responsiveness by the understorey species. This is a quite different forest understorey from one made up of typically shade-tolerant (and slow growing) understorey species, a forest composition expected under climate regimes with little or no drought perturbation (Newbery et al., 1992, 1999). There is no direct evidence of major leaf loss in the understorey though. Shed canopy leaves may also have provided an additional nutrient input which aided understorey recovery. That moderate drought perturbation leads to improved growth conditions in rain forest, especially for the lower canopy and understorey, was also seen in Panama in 1998 (Condit et al., 2004; Wright, 2005). At their site mortality was not elevated either, despite the ENSO event being otherwise severe in the region. By contrast, the effects of the 1983 event were harsher in Panama and the timing of the ENSO with respect to the regular dry season in Central America is clearly very important. This seasonality not being present in Borneo highlights an essential difference between the forested regions and their two environments. To conclude, we have demonstrated that the understorey at Danum is very reactive in terms of growth. Indeed in evolutionary terms it would be counterintuitive that a forest so long subject to such perturbations (forming the red colour to environmental noise) did not show such species’ adaptations and facilitation. Acknowledgements We thank the Danum Valley Management Committee for allowing us to work at Danum, the Economic Planning Unit of the Prime Minister’s Office for permission to undertake research in Malaysia, R.C. Ong and the Herbarium of the Sabah Forest Department for, respectively, facilitating the project and continued taxonomic collaboration; the staff and field assistants of the Danum station for support; E.J.F. Campbell, A. Hämmerli, D.M. Kennedy, H. Petol and M.J. Still for their inputs to the 1986, 1996 and 1999 enumerations; R.P.D. Walsh for access to recent climate data; D.A. Clark, P. Stoll and three anonymous reviewers for comments on the manuscript; and the Swiss National Science Foundation (Grant# 31003A–110250; 2006–2010) and the University of Bern for financial support. Appendices A and B. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2011.07.030. References Beaman, R.S., Beaman, J.H., Marsh, C.W., Woods, P.V., 1985. Drought and forest fires in Sabah in 1983. Sabah Soc. J. 8, 10–30. Bennett, R.J., Chorley, R.J., 1978. Environmental Systems: Philosophy, Analysis and Control. Methuen, London. Blackman, V.H., 1919. The compound interest law of plant growth. Ann. Bot. 33, 353–360.

D.M. Newbery et al. / Forest Ecology and Management 262 (2011) 2095–2107 Boyd, D.S., Foody, G.M., Phipps, P.C., 2006. Dynamics of ENSO drought events on Sabah rainforests observed by NOAA AVHRR. Int. J. Remote Sens. 27, 2197– 2219. Cane, M.A., 2005. The evolution of El Nino, past and future. Earth Planet. Sci. Lett. 230, 227–240. Chen, D., Cane, M.A., Kaplan, A., Zeblak, S.E., Huang, D., 2004. Predicting of El Nino over the past 148 years. Nature 428, 733–736. Clark, D.A., Clark, D.B., 1999. Assessing the growth of tropical rain forest trees: issues for forest modeling and management. Ecol. Appl. 9, 981–997. Clark, D.A., Clark, D.B., 2011. Assessing tropical forests’ climatic sensitivities with long-term data. Biotropica 43, 31–40. Condit, R., Aguilar, S., Hernandez, A., Perez, R., Lao, S., Angehr, G., Hubbell, S.P., et al., 2004. Tropical forest dynamics across a rainfall gradient and the impact of an El Nino dry season. J. Trop. Ecol. 20, 51–72. Condit, R., Ashton, P., Bunyavejchewin, S., Dattaraja, H.S., Davies, S., Esufali, S., Ewango, C., et al., 2006. The importance of demographic niches to tree diversity. Science 313, 98–101. Cuddington, K.M., Yodzis, P., 1999. Black noise and population persistence. Proc. R. Soc. Lond. B 266, 969–973. Evans, G.C., 1972. The Quantitative Analysis of Plant Growth. Blackwell Scientific Publications, Oxford. Feeley, K.J., Wright, S.J., Surpardi, M.N.N., Kassim, A.R., Davies, S.J., 2007. Decelerating growth in tropical trees. Ecol. Lett. 10, 461–469. Feeley, K.J., Davies, S.J., Perez, R., Hubbell, S.P., Foster, R.B., 2011. Directional changes in species composition of a tropical forest. Ecology 92, 871–882. Gibbons, J.M., Newbery, D.M., 2002. Drought avoidance and the effect of local topography on trees in the understorey of Bornean lowland rain forest. Plant Ecol. 164, 1–18. Halley, J.M., 1996. Ecology, evolution and 1/f-noise. Trends Ecol. Evol. 11, 33–37. Halley, J.M., 2007. How do scale and sampling resolution affect perceived ecological variability and redness? In: Vasseur, D.A., McCann, K.S. (Eds.), The Impact of Environmental Variability on Ecological Systems. Springer, Dordrecht, Netherlands, pp. 17–40. Halley, J.M., Inchausti, P., 2004. The increasing importance of 1/f-noises as models of ecological variability. Fluct. Noise Lett. 4, R1–R26. Halley, J.M., Kunin, V.E., 1999. Extinction risk and the 1/f family of noise models. Theor. Popul. Biol. 56, 215–230. Hulme, M., Viner, D., 1998. A climate change scenario for the tropics. Clim. Change 39, 145–176. Hunt, R., 1978. Plant Growth Analysis. E. Arnold, London. Jonzen, N., Lundberg, P., Ranta, E., Kaitala, V., 2002. The irreducible uncertainty of the demography–environment interaction in ecology. Proc. R. Soc. Lond. B 269, 221–225. Kiladis, G.N., Diaz, H.F., 1986. An analysis of the 1877–78 ENSO episode and comparison with 1982–83. Mon. Weather Rev. 114, 1035–1047. Latif, M., Keenlyside, N.S., 2009. El Nino/Southern oscillation response to global warming. Proc. Natl. Acad. Sci. USA 106, 20578–20583. Lambers, H., Chapin, F.S., Pons, T.L., 1998. Plant Physiological Ecology. Springer Verlag, New York. Loreau, M., 2010. From Populations to Ecosystems. Princeton University Press, NJ, Princeton. Lingenfelder, M., Newbery, D.M., 2009. On the detection of dynamic responses in a drought-perturbed tropical rainforest in Borneo. Plant Ecol. 201, 267–290. Lundberg, P., Ranta, E., Ripa, J., Kaitala, V., 2000. Population variability in space and time. Trends Ecol. Evol. 15, 460–464. Malhi, Y., Wright, J., 2004. Spatial patterns and recent trends in the climate of tropical rainforest regions. Proc. Trans. R. Soc. Lond. B 359, 311–329. Markgraf, V., Diaz, H.F., 2000. The past ENSO record: a synthesis. In: Diaz, H.F., Markgraf, V. (Eds.), El Nino and the Southern Oscillation. Cambridge University Press, Cambridge, pp. 465–488. May, R.M., 1974. Stability and Complexity in Model Ecosystems. Princeton University Press, NJ, Princeton. May, R.M., 2001. Introduction to the Princeton landmarks in biology edition. In: Stability and Complexity in Model Ecosystems. Princeton University Press, NJ, Princeton, pp. xi–xxxiv.

2107

McPhaden, M.J., 1999. Genesis and evolution of the 1997–98 El Nino. Science 283, 950–954. McPhaden, M.J., 2007. El Nino and La Nina: physical mechanisms and climate impacts. In: Vasseur, D.A., McCann, K.S. (Eds.), The Impact of Environmental Variability on Ecological Systems. Springer, Dordrecht, Netherlands, pp. 1–16. Nakagawa, M., Tanaka, K., Nakashizuka, T., Ohkubo, T., Kato, T., Maeda, T., Sato, K., et al., 2000. Impact of severe drought associated with the 1997–1998 El Nino in a tropical forest in Sarawak. J. Trop. Ecol. 16, 355–367. Neubert, M.G., Caswell, H., Solow, A.R., 2009. Detecting reactivity. Ecology 90, 2683– 2688. Neubert, M.G., Caswell, H., 1997. Alternatives to resilience for measuring the responses of ecological systems to perturbations. Ecology 78, 653–665. Newbery, D.M., Campbell, E.J.F., Lee, Y.F., Ridsdale, C.E., Still, M.J., 1992. Primary lowland dipterocarp forest at Danum Valley, Sabah, Malaysia: structure, relative abundance and family composition. Proc. Trans. R. Soc. Lond. B 335, 341–356. Newbery, D.M., Campbell, E.J.F., Proctor, J., Still, M.J., 1996. Primary lowland dipterocarp forest at Danum Valley, Sabah, Malaysia. Species composition and patterns in the understorey. Vegetatio 122, 193–220. Newbery, D.M., Kennedy, D.N., Petol, G.H., Madani, L., Ridsdale, C.E., 1999. Primary forest dynamics in lowland dipterocarp forest at Danum Valley, Sabah, Malaysia, and the role of the understorey. Proc. Trans. R. Soc. Lond. B 354, 1763–1782. Newbery, D.M., Lingenfelder, M., 2004. Resistance of a lowland rain forest to increasing drought intensity in Sabah, Borneo. J. Trop. Ecol. 20, 613–624. Newbery, D.M., Lingenfelder, M., 2009. Plurality of tree species responses to drought perturbation in Bornean tropical rain forest. Plant Ecol. 201, 147–167. Payne, R.W., Harding, S.A., Murray, D.A., et al., 2009. GenStat Release 12 Reference Manual. VSN International, Hemel Hempstead, UK. Pearcy, R.W., Ehleringer, J., Mooney, H.A., Rundel, P.W. (Eds.), 1989. Plant Physiological Ecology. Chapman and Hall, London. Petchey, O.L., Gonzalez, A., Wilson, H.B., 1997. Effects on population persistence: the interaction between environmental noise colour, intraspecific competition and space. Proc. R. Soc. Lond. B 264, 1841–1847. R Development Core Team, 2010. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Ripa, J., Lundberg, P., 1996. Noise colour and the risk of population extinction. Proc. R. Soc. Lond. B 263, 1751–1753. Slik, J.W.F., 2004. El Nino droughts and their effects on tree species composition and diversity in tropical rain forests. Oecologia 141, 114–120. Solé, R.V., Bascompte, J., 2006. Self-Organization in Complex Ecosystems. Princeton University Press, NJ, Princeton. Steele, J.H., 1985. A comparison of terrestrial and marine ecological systems. Nature 313, 355–358. Stoll, P., Newbery, D.M., 2005. Evidence of species-specific neighborhood effects in the Dipterocarpaceae of a Bornean forest. Ecology 86, 3048–3062. Timmermann, A., Oberhuber, J., Bacher, A., Esch, M., Latif, M., Roeckner, E., 1999. Increased El Nino frequency in a climate model forced by future greenhouse warming. Nature 398, 694–697. Vasseur, D.A., Yodzis, P., 2004. The color of environmental noise. Ecology 85, 1146– 1152. Vasseur, D.A., McCann, K.S. (Eds.), 2007. The impact of environmental variability on ecological systems. Springer, Dordrecht, Netherlands. Walsh, R.P.D., 1996. Drought frequency changes in Sabah and adjacent parts of northern Borneo since the late nineteenth century and possible implications for tropical rain forest dynamics. J. Trop. Ecol. 12, 385–407. Walsh, R.P.D., Newbery, D.M., 1999. The ecoclimatology of Danum, Sabah, in the context of the world’s rainforest regions, with particular reference to dry periods and their impact. Proc. Trans. R. Soc. B 354, 1869–1883. Walsh, R.P.D., Bidin, K., Blake, W.H., Chappell, N.A., Clarke, M.A., Douglas, I., et al., 2011. Rainforest hydrological and erosional systems and their long-term response to forest disturbance and climate change. Proc. Trans. R. Soc. B. Wright, S.J., 2005. The El Nino Southern Oscillation influences tree performance in tropical rainforests. In: Bermingham, E., Dick, C.W., Moritz, C. (Eds.), Tropical Rainforest: Part, Present and Future. University of Chicago Press, Chicago, USA, pp. 295–310.