Our study site (ca. 750 km2) is part of the Mawas Conservation Area (latitude -2.496 to -2.033∘ N, longitude 114.400 to 114.599∘ E), in the Indonesian province of Central Kalimantan (Fig. 1). The area covers a peat dome whose depth exceeds 12 m in places (KFCP, 2009) and is bordered by two rivers; the major Kapuas river in the west is adjacent to shallow peat and the smaller Mantangai river in the east cuts through deep peat and must have developed after the dome had formed. Rainfall is 3574 mm per year (mean from 1990 to 2011, source: FetchClimate, 2012) with a drier season in June–October.

Much of the area was selectively logged from 1980 to 2000 (Englhart et al., 2013; Gaveau et al., 2014), and an agricultural development project destroyed most of the southern section of the peatland between 1996 and 1999 (the Mega Rice Project, see Aldhous, 2004). Selective illegal timber extraction has persisted despite the area becoming legally protected for conservation (hutan konservasi and hutan lindung) in 2003 (BOS Foundation, 2008; Englhart et al., 2013; Franke et al., 2012). Where logging records are unavailable, historical satellite imagery is often used to retrace the spread of major logging roads through time (Bryan et al., 2013; Gaveau et al., 2014).

We mapped forest cover and human-made linear features corresponding to logging routes (i.e. light railways, trails and canals) using Landsat satellite imagery from 1994 to 2013 processed with CLASlite, a freely available software that performs spectral un-mixing on satellite images (Supplement). CLASlite renders sub-pixel fractional cover information that enabled the identification of logging routes characterized by high fractions of soil or dead vegetation (Asner, 2009). Our local logging route map is similar to the Borneo-wide map of Gaveau et al. (2014), except that we have included additional logging routes resulting from illegal timber extraction after 2000. Forested areas within 500 m of a logging route were classified as selectively logged; the rationale being that mean canopy height maps (measured from ALS) indicate a recovery of canopy height after 500 m. Furthermore, logging operations were reported to extend to 500 m from railways in PSF (Franke et al., 2012) (Supplement). Forest within 5 km of the Kapuas river could not be classified as “old-growth” because local villagers have traditional land rights in that area, and make use of the forests (KFCP, 2009). Since 54 % of that area was interspersed with logging routes, it was classified as “logged”.

ALS data were collected during the dry season of 2011 (15 August to 14 October) with an Optech Orion M200 laser scanner at maximum half scan angle of 11∘ and with a calculated point density of 2.8 points m-2 (full flight specifications given in Supplement Table S2). TIFFS was used to filter the point cloud into ground and object returns (Chen et al., 2007) and to create a digital elevation model (DEM) from ground returns and a digital surface model (DSM) from first returns, both with 1 m pixel spatial resolution. Subtracting the DEM from the DSM resulted in a canopy height model (CHM). We used the vertical distribution of object returns in the ALS point cloud as a proxy for the vertical canopy profile (Asner et al., 2008, 2014). Object return heights were normalized against ground returns and we counted the number of returns within volumetric pixels (voxels) of 20 × 20 m spatial and 1 m vertical resolution, from 0 m up to 40 m above-ground (maximum tree height). Subsequently, the number of returns in each voxel was divided by the sum of all returns in the same vertical column in order to yield a percentage of ALS returns within each slice of the vertical profile (Asner et al., 2008, 2014).

A total of 100 virtual plots of 1 × 1 km were positioned throughout the research area to yield a good coverage of the landscape and avoid having plots crossing land cover boundaries (Fig. S4 in Supplement). Using the map of logged and unlogged areas (Fig. 1) we laid out plots in a random stratified way: 53 plots were located in areas having undergone past concessionary and recent illegal selective logging (henceforth “logged”) and 47 plots in areas unaffected by main logging routes (henceforth “old-growth forest”). Within each plot, the following canopy height and canopy gap metrics were measured using the ALS point cloud or the CHM (summarized in Table 1). All percentage maps and CHM manipulations and measures were done in ArcGIS 10.2 (ESRI, 2013).

Along the whole peat depth gradient and in both old-growth and logged plots, canopy top height decreased by 1 m for each metre of added peat depth (Fig. 3a, Supplement). In an independent data set of more than 300 peat depth measurements and associated canopy top height measurements, canopy top height was not affected by logging (Supplement), suggesting that some large trees (presumably of low commercial value) were left within the plots. The fact that canopy top height was unaffected by logging meant that we could infer peat depth from canopy top height in plots where this information was missing (Supplement). The canopy shape, derived from the complete ALS point cloud, did not change along the peat depth gradient in old-growth forest (grey line, Fig. 3b) suggesting that the height of the main canopy volume decreased in parallel to canopy top height (Fig. 3a).

Canopy gap metrics of old-growth forest also significantly changed along the peat depth gradient. Gap metrics in cross-sections around 8 m above-ground were the most responsive to peat depth and logging effects. The canopy vertical profiles (Fig. 3c) reveal that gaps at 8 m above-ground are clearly located below the bulk of the canopy volume and thus are more likely to have been created by tree mortality rather than just being open spaces between crowns. We hence use the 8 m cross-section to illustrate findings and give full details for all cross-sections in Tables S3 and S4. The mean gap size and gap fraction of old-growth forests decreased with increasing peat depth (grey lines in Fig. 4a–b) in the 8 m height cross-section. The GSFD scaling coefficient (α) became larger with increasing peat depth, indicating an increasing proportion of small gaps (Fig. 4c). The GSFD transition parameter, θ, decreased significantly with peat depth for cross-sections up to 8 m height above-ground (Table S3), but the trend was not statistically significant in the 8 m cross-section (Fig. 4d, Table S3). On average, 6 % of the total gap area was located above θ in the 8 m cross-section, giving support for the finite scaling distribution used here. Negative correlations between α and θ in cross-sections ≥6 m height (Pearson correlation coefficient r=-0.25 to 0.35, p=0.02 to 0.67) indicated that θ was greatest in sites containing large gaps. From cross-sections ≥ 9 m height, θ was not different from zero (Table S3) and the GSFD was described by a power law.

Canopy top height accounted for a large proportion of the variation in canopy gap metrics along the peat dome (recalling that peat depth is negatively related with canopy top height and mean gap area) and was linearly related to mean gap size (Fig. 5a, R2=0.82, p<0.001) and to α (Fig. 5b, R2=0.75, p<0.001) (Table S5).

Selective logging altered both canopy height and canopy gap sizes along the peat dome, especially for higher cross-sections (model M2 or M3 selected). As already described, logging did not influence canopy top height (Fig. 3a). However a marked decrease of canopy shape was observed (Fig. 3b), indicating the removal of canopy volume in logged plots. In the 8 m cross-section, logged plots had larger gaps, a higher gap fraction and a higher proportion of large gaps (smaller α) (red lines, Fig. 4a–c). The transition parameter θ was not significantly larger in logged plots (Fig. 4d). Logging effects were usually observed in height cross-sections from 5 m, and with greater variance among plots with increasing height above-ground (Fig. S6 and Table S3).

Because of unequal effects on canopy top height and gaps, we no longer observed the tight relationships (marked decrease in R2) among canopy top height as an explanatory variable and mean gap area (Fig. 5a, R2=0.28, p<0.001) or α (Fig. 5b, R2=0.38, p<0.001) which we found in old-growth forest (Table S5). This explains the absence of relationship between peat depth and gap metrics in the first half of the peat depth gradient (Fig. 4a–d).

There was limited evidence that logging route density within logged areas had an influence on canopy structure. The logging pressure indices (LPI) did not explain differences in canopy shape parameter, gap fraction, α or θ in areas that we had identified as logged. However, we found that the cumulative LPI increased mean gap size by < 10 % in the 2 m and 3 m cross-sections (Table S6). This indicates that heavier logging in areas with dense logging route networks increased the average size of gaps reaching to the ground irrespective of logging route age.

Logging had a constant effect on canopy shape across the peat dome (Fig. 3b; model M2 selected), but had differing effects on canopy gap metrics except θ (Fig. 4a–c; model M3 selected). Significant interactions between logging and peat depth effects were detected for mean gap area, gap fraction and α in the 8 m cross-section. In all cases, canopy gaps showed a greater logging effect when on deeper peat. In other words, the canopy of logged PSF on intermediate peat depth (6 m) had already recovered to structural characteristics similar to those of old-growth forest while logged forests on deep peat (12 m) exhibited a more strongly altered canopy gap structure (larger gaps in average, higher gap fraction, larger proportion of large gaps) relative to old-growth forest (Fig. 4a–c).

We observed a strong decrease in canopy top height (from about 34 m to 23 m) with peat depth, consistent with field observations (Anderson, 1961; Page et al., 1999; Whitmore, 1975) and ALS results from other Southeast Asian peat domes (Kronseder et al., 2012; Boehm et al., 2013), although for unknown reasons the neighbouring Sebangau peat dome bears tall forest (45 m) on deep peat (Page et al., 1999). PSF exhibit limited height development in comparison to neighbouring lowland dipterocarp forests, where emergent trees typically reach up to 60 m in height (Ashton et al., 1992). The canopy vertical profile revealed that the emergent layer is lost with increasing peat depth. Emergent trees are sometimes lost on nutrient-poorer soils (Whitmore, 1975; Kapos et al., 1990; Paoli et al., 2008; but see Ashton et al., 1992) and shallow rooting depth as a result of substrate waterlogging is likely to limit tree height development (Crawford et al., 2003). Similar patterns are observed in flooded vs. terra firma neotropical forest types (Asner et al., 2013; Boyd et al., 2013; Coomes and Grubb, 1996).

Recent applications of airborne laser scanning (ALS) have identified power-law GSFDs in the neotropics (Asner et al., 2013, 2014; Boyd et al., 2013; Espírito-Santo et al., 2014; Kellner and Asner, 2009; Kellner et al., 2009; Lobo and Dalling, 2013) and Hawaii (Kellner and Asner, 2009; Kellner et al., 2011). Our analysis of an Indomalayan tropical PSF landscape finds a very wide range of scaling exponents α ranging from 1.66 to 3.76 across all old-growth sites and canopy cross-sections (Fig. S6c). The largest α yet reported in the literature is found in short forest on deep peat, indicating that this forest type's gap regime is dominated by very small gaps, which might result from small spaces between evenly distributed small crowns and likely infrequent disturbance events. The large range of α values (range width of 2.1 vs. 0.2 to 1.8 in other studies; Asner et al., 2013; Boyd et al., 2013; Kellner and Asner, 2009; Kellner et al., 2011; Lobo and Dalling, 2013) across the peat dome may reflect a strong control of environmental gradients over forest dynamics. This aspect of PSF ecology deserves future scrutiny through the establishment of permanent plots (Lawson et al., 2014) and repeated ALS surveys.

Changes in the vertical forest structure along the peat dome were associated with a decrease in mean gap size, gap area fraction and the proportion of large gaps. We know of only limited evidence from three field-based studies (Bruenig and Droste, 1995; Kapos et al., 1990; Schaik and Mirmanto, 1985) and one ALS-based study (Kellner et al., 2011) reporting lower gap fractions and smaller average gap sizes in nutrient-poor soils than in higher fertility conditions. These gap patterns may arise from both changes in the organization of crowns in the canopy as well as from changing disturbance patterns along the edaphic gradient. First, smaller gap sizes may be due to a loss of large emergent trees and even canopies filled with small crowns on nutrient-poor substrate (Kapos et al., 1990; Paoli et al., 2008). These shorter trees will additionally create smaller canopy openings when dying (Numata et al., 2006). Accordingly, we found a close link of mean gap size and α with canopy top height along the peat depth gradient (Fig. 5). Secondly, small proportions of large gaps (larger α) on deep peats might result from trees dying “on their feet” in low stature forest (Coomes and Grubb, 1996). Large proportions of large gaps (smaller α) on shallow peats suggest that trees forming structured canopies are more likely to damage neighbouring trees when falling over due to natural mortality or exogenous disturbance factors such as wind or lightning (Bruenig and Droste, 1995; Kapos et al., 1990). Such large gaps are also more likely to experience post-disturbance contagion with higher mortality of exposed neighbouring trees through co-damage or stability loss (Jansen et al., 2008). Thirdly, we assume a functional component by which PSF communities see a shift towards more conservative adaptations (Whitmore, 1975) leading to slow individual turnover on low-fertility substrate (Kellner et al., 2011). Functional and structural adaptations lead to different modes of gap formation on different soil types (Coomes and Grubb, 1996; Jans et al., 1993). A positive feedback loop is created since small gaps tend to be closed by shade-tolerant saplings or lateral regrowth, while larger openings are recolonized by short-lived pioneer and light-demanding species (Sist and Nguyen-Thé, 2002).

Environmental gradients are natural laboratories to explore environmental controls over forest structure using ALS. Changes in forest canopy structure along the peat depth gradient are similar to those observed along a substrate age gradient in Hawaii where nutrient limitation switches from N to P over time, with highest resource availability at intermediate soil ages (Kellner et al., 2011): along both gradients the forests are tallest where nutrients are most plentiful within the landscape, and the taller forests have more structured canopies (emergent layer and main canopy) and large canopy gaps. Canopy height decreases with altitude along an Amazon-to-Andes elevation gradient (Asner et al., 2014), but the changes in canopy structure are quite distinct from those observed in the peat swamp and soil chronosequence: the shorter forests here are sparse in trees, and dominated with a dense fern and bamboo understory, the latter having very open canopies with most canopy volume close to the ground and high proportions of large gaps. The use of different definitions of canopy gaps renders comparison of results difficult (Lobo and Dalling, 2014). While GSFD coefficients are insensitive to plot size, especially in forests dominated by small gaps such as PSF, they vary widely with different height thresholds and spatial resolution of the canopy model (Lobo and Dalling, 2014). We chose a small minimum gap size and different height thresholds following the majority of studies recently published (Kellner and Asner, 2009; Kellner et al., 2011; Asner et al., 2013; Boyd et al., 2013; Lobo and Dalling, 2013). If a consensus is found, combining ALS-derived forest structure measurements with ground data of major environmental drivers could open new avenues for researchers to explore ecological processes, e.g. disturbance dynamics, at spatial scales at which such processes take place, rather than being confined to small-scale plot studies.

Anthropogenic disturbance events such as selective concessionary and illegal logging leave long-lasting legacies of altered dynamics, carbon stocks and species composition in tropical forests often visible more than 20 years after activities have stopped (Numata et al., 2006; Sist and Nguyen-Thé, 2002; Slik et al., 2002). Consistent with this, we detected alteration of forest canopy structure 11 years after selective concessionary logging had stopped and, interestingly, recovery was modulated by environmental conditions along the peat dome.

Logged forests harboured an altered vertical structure and larger gaps, a higher gap fraction and lower α from about 6 m above-ground relative to old-growth forest on similar peat depth. Canopy top height remained unaltered after selective logging probably because some tall low-value timber trees remain unharvested, but the relative vertical distribution of canopy volume was reduced by tree removal under logging.

Canopy structure in logged sites did not generally relate to the “logging pressure index” (LPI), except that larger gaps close to the ground were found in areas with dense logging route networks. This effect did not vary with the age of logging routes, which suggests that existing logging routes have slow structural recovery or continue to be used for illegal timber harvesting. Usually, canopy recovery depends strongly on time since logging and on logging intensity (Asner et al., 2004b, 2006; Sist et al., 1998). Logging infrastructure and routes, used here to infer the presence and timing of logging, might however not always be a good predictor of logging effect severity (Asner et al., 2004b). PSF on deep peat was deemed unsuitable for commercial logging operations due to low density of poles and fragility of the system (Bruenig and Droste, 1995). Yet we detected concessionary logging railways on deep peat in our study area, and we are developing new techniques to better monitor illegal logging (unpublished data). Subsequent ALS research should preferably be carried out in logging concessions where timing and intensity of logging are well documented (see e.g. Andersen et al., 2013; d'Oliveira et al., 2012). Since the logging pressure was relatively homogenous along the peat depth gradient and canopy structure did not respond to variation in logging pressure, we can interpret observed differences in canopy gap patterns between logged and old-growth plots as mostly related to inherent differential forest recovery rates along the peat dome.

Canopy structural responses to selective logging were influenced by peat depth; a likely explanation is slower recovery rates of forests growing on nutrient-depleted and waterlogged substrates in the centre of peat domes. Gap metrics were most sensitive to differential recovery across the peat dome. In particular, a clear segregation in GSFD scaling exponent α was observed between old-growth and logged plots on deep peat; large differences in the scaling relationships of undisturbed vs. disturbed systems have previously been related to low resilience in disturbed systems (Kerkhoff and Enquist, 2007). Those forest communities adapted to extreme environmental conditions are unlikely to recover fast following logging because species might have conservative adaptations and grow slowly. Thus recolonization of canopy openings would be very slow.