How resilient are peatland forests to forest degradation and conversion? What is their capacity recover desirable ecosystem states along with associated ecosystem services? Biodiversity is said to enhance ecosystem resilience (Elmqvist et al. 2003). In comparison to non-peat lowland forest, peat swamp forest supports low animal density and diversity (Janzen 1974; Whitten et al. 2000; Posa et al. 2011) due to its low nutrient content and low plant productivity (Bruenig and Droste 1995). Gaither Jr (1994) reported that although bird populations in peat swamp forest had low species diversity and density overall, some species have a higher density than in lowland dipterocarp forests. The uniqueness of the peatland ecosystem is attributed to a swampy ecosystem that is formed on peat soil, which contains high levels of organic carbon from undecomposed deadwood. Peatland soils are usually waterlogged and therefore have high acidity (pH 3–4.5) and low nutrient content.

Ecosystem or community resilience is evaluated by comparing pre-and postdisturbance variables and compositions (Norden et al. 2009; O'Dea and Whittaker 2007; Moretti et al. 2006). Our project used similar methods to compare the characteristics of bird communities on peatland and non-peat lowland by focusing on the following: (1) species richness; (2) feeding guilds; and (3) response of avifauna to disturbance. We analyzed how bird populations responded to rapidly changing patterns of peatland land use. We compared data of bird populations inhabiting several different peatland landscapes in Riau, with those inhabiting non-peat lowland landscapes in Sumatra Island. We then discussed the vulnerability and potential resilience of the peat swamp ecosystem as indicated by avian population response to disturbance.

#### 3.2 Methods

#### 3.2.1 Datasets in Peatland

We used bird population datasets published by Fujita et al. (2016). The study site was located in the Bukit Batu area of the Giam Siak Kecil-Bukit Batu Biosphere Reserve, Riau Province, Indonesia (Fig. 3.1). Observations took place in the following sites: (1) natural peat swamp forest in the Bukit Batu Wildlife Reserve (hereafter referred to as BNF), which is managed by the Forestry Department of Riau; (2) plantation forest of Acacia crassicarpa, which is managed by the companies PT. Bukit Batu Hutani Alam and PT. Sakato Pratama Makmur (referred to as BAF);1 (3) planted rubber (Hevea brasiliensis) forests, which are managed by local people (referred to as BRF); and (4) the residential village area in the Bukit Batu area (referred to as BRV; Fig. 3.2). BNF is dominated by Palaquium sumatranum, Eugenia paludosa, Diospyros hermaphroditica, Calophyllum lowii, and Shorea teysmanniana, with a peat depth of up to 6 m (Gunawan et al. 2012). According to Fujita et al. (2016), a bird survey was conducted in selectively logged forests which maintain a complex and multistory forest structure. Planted acacia forests are pure plantations of Acacia crassicarpa that are established on peatland where canals were dug in grids for drainage and transport purposes. Acacia trees are harvested within

1 "PT." is the abbreviation for Perseroan Terbatas, and it means Limited Liability Company.

Fig. 3.1 Study sites. Abbreviations of study sites on the peatland of Fujita et al. (2016) are as follows: BNF natural peat swamp forest, BAF planted acacia forest, BRF planted rubber forest, BRV residential village. Study sites on non-peat lowland of Thiollay (1995) are TNF natural forest, TRF planted rubber forest, of Fujita et al. (2014) are FNF natural forest; FAF planted acacia forest, of Danielsen and Heegaard (1995) are DNF natural forest, DRF planted rubber forest, DOP oil palm plantation

5–6 years. The Acacia forest monocrop structure is simple—Acacia crassicarpa comprises the canopy layer, with only some ferns on the forest floor. Planted rubber forests, meanwhile, often include dense understories including shrubs, small trees, and saplings of native forest species. Although planted rubber forests range from highly-managed plantations with less understory to extensive forests with more understory trees, they are usually more diversified than industrial plantations. Land use in the residential village area consists of home gardens, rubber plantations, paddy fields, oil palm plantations, secondary forests (mainly Macaranga trees), coconut plantations, and burnt brush land.

Bird surveys were conducted using fixed-radius point count methods in twelve 1 km lines. Three surveys were conducted in each of the four land use categories. In each survey, four points were established at 250 m intervals, and every bird species that was seen or heard within a 25 m radius was recorded during the 20-min observation period. Observations were conducted in the morning and evening in March, May, and October 2011. In total, 34 censuses were made in the natural peat swamp forest (BNF), and 48 censuses were made in other sites (BAF, BRF, and BRV).

Fig. 3.2 Typical land use in peatland in Bukit Batu, Riau: (a) planted acacia forest (BAF); (b) residential village area (BRV); and (c) planted rubber forest (BRF) (Photos taken by Fujita)

#### 3.2.2 Datasets in Non-peat Lowland

We selected three papers that surveyed the bird community in non-peat lowland in Sumatra (Table 3.1). The criteria for the paper selection were that the datasets must be from direct observations of birds using either the line-transect or point-count method, both of which are useful in analysis of bird community composition. Thiollay (1995) investigated bird fauna in lowland dipterocarp forests (hereafter referred to as TNF) and planted rubber forests (hereafter referred to as TRF) in the Jambi and West Sumatra provinces in May–June 1991 and July 1992, respectively (Fig. 3.1). The emergent trees (44–55 m) in TNF were mostly Dipterocarpus sp., Shorea sp., and Cesalpiniaceae, with canopy trees (35–45 m), including numerous Dipterocarpaceae, Myristicaceae, Sapotaceae, and fig trees (Ficus sp.). TRF consists mainly of para-rubber trees, Hevea brasiliensis, which are lower (closed canopy at 20–30 m) and have dense undergrowth. An average stand includes 750 trees per hectare, 65% of which are rubber trees mixed with 12–20 other species of forest and fruit trees. A line-transect survey was performed, where a sample was defined as the first 50 individual birds that were recorded within 20–30 m on either side of the transect. Twenty eight-samples were collected in both TNF and TRF. Since the

Table 3.1 Site description

accessible tracts of the primary forest were limited, all the data from the three different areas were pooled into a single 28-sample set that was assumed to be representative of the western Sumatra natural forest. Counts were made from dawn to 11:00 h and from 15:00 h to sunset outside rainy periods.

Danielsen and Heegaard (1995) investigated bird fauna in both primary and logged lowland dipterocarp forests (hereafter referred to as DNF), planted rubber forests (hereafter referred to as DRF), and oil palm plantations (hereafter referred to as DOP) in Bukit Tigapuluh, Riau, and Jambi province in July–September 1991 (Fig. 3.1). The most prevalent tree families in DNF were Euphorbiaceae, Lauraceae, Burseraceae, and Dipteroearpaceae, which comprise a multistory forest structure, with a 26–38 m canopy layer, 18–26 m middle layer, and 10–21 m lower layer. The height of the canopy in DRF was 15–20 m, with emergents reaching 25 m, and 36% of the trees over 10 cm diameter reaching breast height (DBH) were estimated to be para-rubber tree (Hevea brasiliensis). Undergrowth consisted of 1–2 m tall perennials and thickets of bamboo Dendrocalamus sp.

The DOP plantation was 10,000 ha and consisted of mature oil palms, Elaeis guineensis. The average height of the palms was 11 m, and the density was 162 trees per ha. Within 1.5 m of the palms, an area was covered by dead palm leaves and ferns or no vegetation. The authors used a variable-distance line-transect method supplemented by systematic mist-netting. A line-transect survey route of 2 km was cut in a straight line in each habitat every 250 m. The route was walked by one person with a speed of 250 m per 15 min, including brief stopovers. The route was surveyed 4 times per day. In DNF and DRF, the route was surveyed for 40 h, while in DOP, the route was only surveyed for 20 h. Mist netting was carried out with 15–20 nets, which were mainly set at a height of 0.5–3.0 m for three to four consecutive days. The effort was 10,200–15,500 net-meter hours in DNF and DRF and only 3300 net-meter hours in DOP.

The third paper used to establish the present dataset, by Fujita et al. (2014), examined bird fauna in a lowland Acacia mangium plantation (FAF) and remnant logged natural forest (FNF). The height of the acacia canopy in FAF for the 4-yearold planted acacia forest was 10–20 m with less understory (1–10 m) vegetation, and the forest floor was occasionally covered by ferns or sparse scrub. The forest floor of the 1-year-old planted acacia forest had no vegetation other than 3–5 m of planted A. mangium. Most of the points in FNF consisted of trees that were 20–30 m tall and scattered with multistory vegetation. The other two sites of conserved logged forests (CF-07, CF-08) were burnt during a forest fire, and no living tall trees could be seen; instead, all sites were covered by dense bush.

Fixed-radius point counts were conducted in October–December 2007 and July– August 2008. Twenty survey points for FAF in the 1- and 4-year-old planted acacia forests and 12 survey points for FNF were selected. At each point, the bird species that were observed within a 25 m radius were recorded in the 10-min census. The number of censuses totaled 303 in FAF and 197 in FNF.

#### 3.2.3 Feeding Guild

To compare and evaluate bird communities and forest ecosystem in terms of ecological function, feeding guilds of birds were used in the following analysis. A feeding guild is defined as the groups of species that have similar feeding habits. The following 17 feeding guild categories were used for the analysis: A-F, arboreal frugivore; AFG-I, arboreal foliage gleaning insectivore; AFG-IF, arboreal foliage gleaning insectivore–frugivore; A-FP, arboreal frugivore–predator; A-I, aerial insectivore; BG-I, bark gleaning insectivore; M-IP, miscellaneous insectivore–piscivore; NF, nectarivore-frugivore; NI, nectarivore-insectivore; NIF, nectarivore-insectivorefrugivore; R, raptor; S-I, sallying insectivore; SSG-I, sallying substrate gleaning insectivore; T-F, terrestrial frugivore; T-G, terrestrial granivore; T-I, terrestrial insectivore; and T-IF, terrestrial insectivore–frugivore. Each species was categorized in one of the feeding guilds, with reference to Lambert (1992), Styring et al. (2011), and Smythies (1999). Species previously unreferred to a feeding guild were categorized according to the feeding guild of the nearest species in the same genus.

#### 3.2.4 Statistical Analysis

To compare the species richness at each site, all datasets were merged, and abundance data was converted to presence-absence data. Water birds, night birds, and swifts (excluding tree swifts) were omitted from the list. Scientific names followed the nomenclature of Sibley and Monroe (1990). Since observation efforts differ among sites, a direct comparison of species richness or number must be treated with caution. Therefore, we used the nonparametric Jackknife estimator of species richness to compare bird diversity among the sites. Jackknife 1 was calculated for the data of Bukit Batu and Fujita et al. (2014) using the software EstimateS ver 9.1 (Colwell 2013). Jackknife 1 was used since it has been widely used for years, including by Thiollay (1995). In this manner it was possible to compare data from at least three sites, with the exception of the dataset of Danielsen and Heegaard (1995).

To compare the species composition among the study sites, the ordination of sites and habitat types was performed against the presence-absence data. This was achieved using nonmetric multidimensional scaling (NMDS) with the package "vegan" (Oksanen et al. 2015) in the statistical software R version 3.2.2 (R Core Team 2015). NMDS was performed with Bray–Curtis as a dissimilarity index, and a maximum 100 times of iteration was conducted to obtain better ordination scores. Additionally, the species score against each axis was calculated, although only abundant species appeared with a label when several species appeared in the small area.

#### 3.3 Results and Discussion

## 3.3.1 Bird Species Diversity on Peatland and Non-peat Lowland

The overall species number in the forests on peatland was lower than that of forests on non-peat lowland. The total number of species in peatland in the Bukit Batu area was 44 in the natural peat swamp forest (BNF), 20 in the planted acacia forest (BAF), 38 in the planted rubber forest (BRF), and 51 in the residential village area (BRV). In the forests in the non-peat lowland of Thiollay (1995), the number of species was 180 in natural forest (TNF) and 104 in the planted rubber forest (TRF). In the study by Danielsen and Heegaard (1995), there were 147 in the natural forest (DNF), 90 in the planted rubber forest (DRF), and 24 in the oil palm plantation (DOP). In the study by Fujita et al. (2014), there were 82 in the natural forest (FNF) and 63 in the planted acacia forest (FAF).

Out of 283 bird species in total, 101 species were recorded in forests on peatland in Bukit Batu, whereas in forests on non-peat lowland, 267 species were reported, and 85 species were seen in both peatland and non-peat lowland. Out of the 267 species that were seen in the forests on non-peat lowland, 182 species were not recorded in the forests on peatland. However, out of the 101 species on peatland, 16 species were seen only on peatland, of which four species (Prionochilus thoracicus, Ficedula zanthopygia, Malacocincla abbotti, and Nectarinia calcosthetha) were specifically seen only in natural forests on peatland (BNF). Of these, P. thoracicus and N. calcosthetha favor swamps or coastal forests as their

Table 3.2 Bird species in common in natural forests in Bukit Batu and other studies. Numbers in italics indicate the number of species in common, whereas the numbers in parentheses indicate Jaccard's coefficient of community (CC)

a Jaccard's CC was calculated using the following formula: CC = c/(a + b - c), where a is the number of species in site A, b is the number of species in site B, and c is the number of common species between site A and B. A larger number indicates a higher degree of similarity between the two sites

habitat. Additionally, we observed several species that prefer swamps, including Malacopteron albogulare and Setornis criniger (Fujita et al. 2012), although they are not listed in the point-count survey by Fujita et al. (2016). Gaither Jr (1994) reported that several bird species, including M. albogulare, were found in higher density in peat swamp forests compared to non-peat lowland forests. Although the species richness is low compared to forests on non-peat lowland, forests on peatland could be home to several bird species that favor a swampy environment or species that avoid the highly competitive pressure in forests on non-peat lowland.

Jaccard's coefficient of community (CC) index, which represents the community similarity between two sites, indicates that the peatland bird community has a low similarity (0.167–0.248) with other non-peat lowland bird communities, whereas the non-peat lowland bird community has a higher similarity (0.242–0.493) among sites (Table 3.2).

Since the efforts and methods of observation differ among sites, it is not possible to compare species richness directly. The Jackknife estimator of species richness, the Jack 1 mean, indicates the true species richness of natural forest in Bukit Batu (BNF) as 77.2, which is 33 species greater than the observed species richness (Table 3.3). Compared to natural forests on non-peat lowland, of which the estimated species richness is 241 species in TNF and 114.8 in FNF, natural forests on peatland have a lower species richness.

These measurements indicate poor species diversity in peat swamp forests, which follows the hypothesis that peat swamp forests support depauperate animal communities due to the cascading influence of poor soils (Janzen 1974). Nevertheless, the peat swamp forest is a unique ecosystem supporting several bird species that are not present in other ecosystems, including forests on non-peat lowland.

These direct comparisons of landcover and bird populations must be considered with caution for several reasons. First, observation efforts differ significantly in the

Table 3.3 Comparison of species richness and estimators of natural forest among four sites. The proportion of observed richness to the computed estimates is shown in parentheses

a A sample is defined as the first 50 individual birds that were recorded along a random footpath within 20–30 m on either side of the transect. Fifty individual samples were accumulated within 1– 4 h along a track

different studies—up to 10 times in observation hours (Table 3.3). The lower observation time in the natural forest on peatland is due to limited accessibility, time, and labor power, all of which may be linked to the lower number of species observed. Comparisons of estimators of species richness, such as Jackknife, have solved this problem to some extent. Second, the observation methods differed among sites and included line transect, point count, and mist netting. Even the line-transect method differed slightly among sites, as Thiollay (1995) counted 50 individuals from the beginning, with no regard to the length of the line, whereas Danielsen and Heegaard (1995) walked the 250 m line and counted all birds observed. Third, habitat complexity differed among sites, as Thiollay (1995) merged all data from several patches from different regions and treated it as a single dataset for the primary forest. The regions include hill forests, which resulted in the presence of montane species and increased the overall species richness. Moreover, regarding Bukit Batu, all study points of natural peat swamp forest (BNF) were selectively logged a few years before the survey, perhaps affecting the study results. In short, such discrepancies in datasets indicates that the hypothesis that peatlands support poor bird species diversity still requires further intensive research.

#### 3.3.2 Feeding Guild Change in Disturbed Sites on Peatland

In the disturbed sites of peatland in Bukit Batu, namely planted acacia forest (BAF), planted rubber forest (BRF), and residential village (BRV), a decline in the sallying insectivores (S-I and SSG-I) was observed (Table 3.4). This decline was also observed in non-peat lowland forest. BRV was characterized by more granivores (T-G) and terrestrial doves (T-IF), which reflect the abundance of open spaces and

#### Table 3.4 Number of species for each feeding guild

grass seeds as their food (Fig. 3.2b). Hornbills (A-FP) appeared more frequently in the residential village than expected. This could be because they were more easily detectable in open spaces than in the forest, since their sound was heard more often in the forested sites than BNF (although not every species was recorded when their call was beyond our observation range of 25 m).

Compared to natural forests on non-peat lowland (TNF, DNF, and FNF), natural forest in Bukit Batu (BNF) was characterized by fewer terrestrial insectivores (T-I), such as Trichastoma sp., Enicurus sp., Pitta sp., and Napothera sp., and fewer woodpeckers (BG-I) (Table 3.4). Gaither Jr (1994) reported similar results in a mistnet survey of understory birds in peat swamp forests in West Kalimantan. He found litter-gleaning insectivores (T-I in our category), tree foliage-gleaning insectivores (AFG-I in our category), and bark-gleaning insectivores in significantly lower numbers in the peat swamp forest than in the dipterocarp forest. Posa (2011) had partly contradicting results in the peat swamp forest in Central Kalimantan, as six species (8%) of woodpeckers (BG-I) were recorded in peat swamp forests, although litter-gleaning insectivores (T-I in our category) only comprised three species (4%). The abundance of woodpeckers was associated with the number of dead trees in the degraded peat swamp forest due to recent drought stress (Posa 2011). The decline in TI and BGI in Bukit Batu might be the result of logging activity in the natural forest. As recently as 2009, illegal logging has occurred in most of the accessible areas, including our study plots.

The low number of arboreal frugivores (A-F) could be explained by the seasonal change. Gaither Jr (1994) reported that many frugivore species were observed in June in the peat swamp forest, but not in other months.

## 3.3.3 Shift in Bird Community Composition along the Disturbance

Many fewer species were found in disturbed sites than in the natural peatland forests in Bukit Batu. Out of the 44 species found in natural forests in Bukit Batu (BNF), only eight species in planted acacia forest (BAF), 12 species in the planted rubber forest (BRF), and nine species in residential village (BRV) were shared, which indicated that more than 70% of species were lost in the disturbed sites. However, in non-peat lowland, 88 species in the planted rubber forest (TRF) out of 180 species in the natural forest (TNF), 66 species in the planted rubber forest (DRF) out of 147 species in natural forest (DNF), and 42 species in the planted acacia forest (FAF) out of 82 species in natural forest (FNF) were found, which account for approximately 50% of the species loss in the disturbed sites. The oil palm plantation showed a drastic species loss of up to 94%, as only eight species were found in DOP, in comparison to 147 species in the natural forest (DNF).

The NMDS analysis showed that the bird community composition in forests on peatland is unique compared to forests on non-peat lowland (Fig. 3.3a). Points of

Fig. 3.3 Bird community similarities using an NMDS ordination of study points (a) and species (b). (a) Each point corresponds to a study point. Abbreviations in the legends are as follows: BNF natural peat swamp forest in Bukit Batu (closed triangle), BAF planted acacia forest in Bukit Batu (closed circle), BRF planted rubber forest in Bukit Batu (closed square), BRV residential village in Bukit Batu (star), TNF natural forest of Thiollay 1995 (pale gray triangle), TRF planted rubber of

non-peat lowland (TNF, TRF, DNF, DRF, DOP, FNF, and FAF) showed a higher NMDS2 value than points of peatland, except for BRV, which is BNF, BAF, and BRF. This indicates that peatland has different avifauna compared to the forests on non-peat lowland. BRV showed a similar NMDS2 value as that of non-peat lowland; this might have resulted from either the high modification to the village or the presence of non-peat soil in the villages, which are located on the coastline. The NMDS2 axis was negatively correlated with the presence of Nectarinia calcosthetha, which is restricted to peat swamp forests (Fig. 3.3b).

While the NMDS2 axis divided forests on non-peat lowland and peatland, NMDS1 axes showed the degree of disturbance. On both peatland and non-peat lowland, the natural forest points (BNF, TNF, DNF, and FNF) showed lower NMDS1 values than disturbed points, such as planted acacia forest, planted rubber forest, oil palm plantation, and residential village (Fig. 3.3a, direction of disturbance shown in the arrows for each site). Species that had a positive correlation with the NMDS1 axis include Cotumix chinensis and Turnix suscitator, both of which are open space species, and species that had a negative correlation include Calyptomena viridis and Trichastoma bicolor, both of which are species inhabiting less disturbed natural forests (Fig. 3.3b).

Disturbed points on peatland in Bukit Batu (BAF, BRF) showed a higher NMDS1 value and NMDS2 value compared to natural forests (BNF). This indicates that when natural forests are converted to planted acacia and rubber forests bird communities change accordingly, shifting toward the avifauna of non-peat lowlands. For example, Nectarinia calcostetha, which is found in natural peatland forests, has not been recorded in other habitat types. Disturbance of peatland therefore will likely lead to the homogenization of avifauna and loss of uniqueness, which would then lead to a loss in overall biodiversity.

Peatland-based bird community composition responded sensitively to land use change. Euclidean distances of the NMDS values of community composition between natural forest and planted rubber forest in non-peat lowland were 0.52 in TNF-TRF, 0.47 in DNF-DRF, and 0.93 in BNF-BRF in peatland, indicating a

Fig. 3.3 (continued) Thiollay 1995 (pale gray square), FNF natural forest of Fujita et al. 2014 (open triangle), FAF planted acacia forest of Fujita et al. 2014 (open circle), DNF natural forest of Danielsen and Heegaard 1995 (dark gray triangle), DRF planted rubber forest of Danielsen and Heegaard 1995 (dark gray square), DOP oil palm plantation of Danielsen and Heegaard 1995 (cross in open circle). The arrows indicate the directions from the natural forest to the disturbed habitat at each site. The values near the arrows indicate the Euclidean distances calculated from the NMDS 1 and 2 values. (b) In the species plot, only abundant species are labeled where several species are clustered. Abbreviations for the species names are as follows: Amauphoe Amaurornis phoenicurus, Bucevigi Buceros vigil, Celebrac Celeus brachyurus, Copssaul Copsychus saularis, Corvmacr Corvus macrorhynchos, Cotuchin Cotumix chinensis, Dicrpara Dicrurus paradiseus, Macrgula Macronous gularis, Malamagn Malacopteron magnum, Muscdauu Muscicapa dauurica, Nectcalc Nectarinia calcostetha, Orthseri Orthotomus sericeus, Pandhali Pandion haliaetus, Phylbore Phylloscopus borealis, Pycnbrun Pycnonotus brunneus, Rollroul Rollulus rouloul, Spilchee Spilornis cheela, Strechin Streptopelia chinensis, Trercurv Treron curvirostra, Trervern Treron vernans, Tricbico Trichastoma bicolor, Turnsusc Turnix suscitator, Zootinte Zoothera interpres

1.7–1.9-times longer distance in peatland (Fig. 3.3a). As distances on NMDS values indicate differences in community composition, a longer Euclidean distance indicates a larger difference in community composition. Euclidean distances between natural forest and planted acacia forest in non-peat lowland were 0.37 in FNF-FAF and 1.41 in BNF-BAF in peatland, which shows a 3.8-times longer distance in peatland (Fig. 3.3a). Although avifauna clearly responded more strongly to disturbance in peatland, the reason for this vulnerability is unclear. It may be attributed low biodiversity and the idiosyncratic characteristics of the peat swamp ecosystem. To verify the resilience in the peat swamp forest, repetitive data from long-term studies on community composition and analyses from the perspective of landscape ecology are necessary. For example, differences in landscape structure and scale in peatlands might have significant impacts on bird communities, since plantations are often developed on a large scale due to the flat topography and lower utilization by local people.

#### 3.3.4 Conclusion: Peatland Ecosystem for Birds

Our survey and analysis lead us to conclude the following: (1) although the peatland ecosystem has a comparably poorer species diversity than non-peat lowland forest, peatlands contains unique and idiosyncratic avifauna; (2) peat swamp forest avifauna lack terrestrial insectivore and woodpeckers; and (3) avifauna in peatland are sensitive to forest conversion to planted tree forest, and can experience a loss of more than 70% of species. In other words, unique and idiosyncratic avifauna on peatland are only maintained by natural peat swamp forest. Our first two findings support those of Janzen (1974), Gaither Jr (1994), and Posa (2011). The third finding, which has the most stimulating implication, suggests that the peat swamp ecosystem has a higher vulnerability to disturbance compared to non-peat lowland. It is still in the stage of hypothesis. Peat swamp vulnerability may be due to poor biodiversity and the idiosyncratic characteristic of the peat swamp ecosystem. Longterm studies on bird community and landscape ecology are required to evaluate this hypothesis. Nevertheless, our study found several important implications for peatland management, considering that there are limited studies on the effect of peatland disturbance on biodiversity.

Samejima et al. (2016) listed several other bird species detected by camera trap. Lophura erythrophthalma and Melanoperdix niger were both vulnerable species, and they were both restricted to natural peat swamp forest and not observed in the planted acacia forest. This indicates that disturbance has negative effects on these forest-dependent species, and the implication is consistent with our results. To conserve the unique peat swamp avifauna and maintain the ecosystem services that are provided by biodiversity, peat swamp forest should not be converted to plantations and agricultural lands, as peatland ecosystems have low resilience to disturbance.

Acknowledgments This study was partly supported by the JSPS Global COE Program In Search " of Sustainable Humanosphere in Asia."

#### References

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# Chapter 4 Impact of Industrial Tree Plantation on Ground-Dwelling Mammals and Birds in a Peat Swamp Forest in Sumatra

#### Hiromitsu Samejima, Motoko S. Fujita, and Ahmad Muhammad

Abstract Peat swamp forests are one of the unique ecosystems of Southeast Asia. These forests are not only a large carbon stock, but also a refuge for rich biodiversity. To understand the faunal composition and the effect of land-use changes in peat swamp forests, we investigated ground-dwelling mammals and birds using camera traps in a natural peat swamp forest and acacia forests planted in two industrial tree plantations in the Giam Siak Kecil-Bukit Batu Biosphere Reserve, Riau, Indonesia, in the Island of Sumatra.

We obtained a total of 1856 records, comprising 23 species and including 11 vulnerable or endangered species, in ten plots. The range of mean trapping rates (number of records per 100 camera working days) of all animals in each plot in natural peat swamp forests were 9.22–51.85 (mean: 29.16) and 8.75–31.76 (16.42) in the wildlife reserve and protected area of the plantations respectively. The range in planted acacia forest was 2.29–6.38 (4.02). Few species were recorded in the planted acacia forests, and the species composition was different from that in the natural peat swamp forests. These differences indicate that conversion from natural peat swamp forests to planted acacia forest through development of industrial tree plantations resulting in decreased density and species richness of grounddwelling mammals and birds. Because the ground-dwelling mammal and bird community in natural peat swamp forest is vulnerable to land use change, conservation of the remnant natural peat swamp forests and appropriate landscape design of industrial tree plantations are considered important to maintain the ecosystem.

Keywords Peatland · Sumatra · Industrial tree plantation · Camera trap · Mammal · Bird

H. Samejima (✉)

M. S. Fujita Center for Southeast Asian Studies, Kyoto University, Kyoto, Japan

A. Muhammad University of Riau, Pekanbaru, Riau, Indonesia

Institute for Global Environmental Strategies, Hayama, Kanagawa, Japan e-mail<EMAIL_ADDRESS>
#### 4.1 Introduction

Lowland peatlands are a unique ecosystem in Southeast Asia, distributed mostly in the east coast of Sumatra, Central and West Kalimantan, Sarawak, and southern Papua (Hooijer et al. 2010). Peat soil consists mainly of decomposed trunks, branches and roots of trees which have been sedimented over a period of 4000–26,000 years (Page et al. 1999, 2004). Peatlands are nutrient poor and have extremely acidic soil (pH 3.0–4.5) because they are situated away from large rivers that bring mineral nutrients downstream from mountainous areas. The total area of peatlands in Southeast Asia is estimated to be 24.8 million ha, comprising 56% of the global total. Southeast Asian peatlands are estimated to store 68.5 Gt of carbon, comprising 11–14% of the carbon stock in the global peatland (Page et al. 2011).

The natural vegetation of Southeast Asian peatlands is peat swamp forest (Anderson 1961; Bruenig 1990; Posa et al. 2011). Peat swamp forests in Southeast Asia function not only as a significant carbon sink, but also as a refuge for many endangered species in the region. The faunal composition of this vast ecosystem has not been as well studied as the floral composition, however (Gaither Jr 1994; Page et al. 1997; Whitemore 1984). Because of the poor nutrient content of the peat soil and the low primary productivity of the forests, they have been presumed to have lower diversity and abundance of animals than surrounding lowland forest (Janzen 1974; Posa et al. 2011; Whitten et al. 2000). Many endangered species, however, are known to inhabit peat swamp forests (Buckley et al. 2006; Cheyne et al. 2008; Felton et al. 2003; Morrogh-Bernard et al. 2003; Yule 2010). Furthermore, Ehlers Smith and Ehlers Smith (2013), Johnson et al. (2005), and Quinten et al. (2010), indicated that densities of several primate species, including orangutan, in peat swamp forests were higher than those in adjacent lowland forests. Gaither Jr (1994) also detected that some bird species were more abundant in peat swamp forest than in lowland forest.

Recently, ground-dwelling mammals and birds in natural peat swamp forest have been investigated with camera traps. Camera traps are automatic digital cameras equipped with infrared motion sensors that present new methods for investigation of faunal diversity (Cannon et al. 2007; Cheyne et al. 2010; Cheyne and Macdonald 2011; Posa 2011; Kays and Slauson 2008; Rowcliffe and Carbone 2008; McCallum 2012; Burton et al. 2015; Nakashima et al. 2018). Mohd-Azlan (2004) conducted camera-trapping studies in a natural peat swamp forest at Maludam National Park in Sarawak; Cheyne et al. (2010) and Cheyne and Macdonald (2011) conducted the same in the Sebangau National Park in Central Kalimantan; and Posa (2011) along the Kapuas River, Central Kalimantan. However, some of those studies reported only felid and bird species, and the others ran their camera traps for only a limited number of days.

In recent decades, large-scale plantations of fast-growing trees and oil palm have been developed in the peatlands in Southeast Asia (Uryu et al. 2008; Corlett 2009; Yule 2010). Posa et al. (2011) estimated that a minimum of 64% of the historical peat swamp forest in Southeast Asia has been lost so far. The deforestation and drainage of the peatlands results in huge emission of carbon dioxide, which accelerates global warming (Couwenberg et al. 2009). Carbon dioxide emissions from peat swamp

forest degradation in Southeast Asia during 1997–2006 was estimated to be as much as 0.30 Pg C year-1 , or 3% of total global anthropogenic carbon emissions (van der Werf et al. 2009).

Conversion of natural peat swamp forests to industrial tree plantations and oil palm plantations can also cause significant loss of biodiversity, but this phenomenon is not yet well studied (Posa 2011; Posa et al. 2011). Some animal species inhabiting peat swamp forests may be able to adapt to the new vegetation (Meijaard et al. 2010), but plantantions may also lead to serious loss of biodiversity, especially among species vulnerable to development in surrounding lowland forests in the region (Fitzherbert et al. 2008; McShea et al. 2009). Understanding the effects of plantation development on the animal community in peat swamp forests is necessary for proper management of the peat swamp landscape of the region.

To understand the faunal diversity of natural peat swamp forests and the impact of plantation development on these, we investigated species composition and abundance of ground-dwelling, medium to large mammals and birds using camera traps in natural and planted acacia forests in Giam Siak Kecil-Bukit Batu Biosphere Reserve (GSK-BB), Riau Province, Indonesia, in the Island of Sumatra. Some of the results were already published as Samejima et al. (2016).

#### 4.2 Materials and Methods

#### 4.2.1 Study Area

The Giam Siak Kecil-Bukit Batu Biosphere Reserve (GSK-BB) is located along the Strait of Malacca, covering an area of 7053 km2 . This biosphere reserve was established by a private-sector initiative and was registered by UNESCO's Man and Biosphere (MAB) program in 2009. GSK-BB consists of three zones: a core area (1787 km2 ), buffer zone (2224 km2 ), and transition area (3041 km2 ). The core area consists natural peat swamp forest mostly in the Giam Siak Kecil Wildlife Reserve and the Bukit Batu Wildlife Reserve. The buffer zone comprises planted forests of fast-growing tree species (Acacia crassicarpa and A. mangium) inside four industrial tree plantations, called Hutan Tanaman Industri (HTI) in Indonesia. The plantations also have natural forests in areas adjacent to the Wildlife Reserves, which constitute a part of the core area. The natural forest was preserved in accordance of the Ministry of Forestry Decree No.70/Kpts-II/1995 and No.246/Kpts-II/1996, requiring that 10% of a concession area should be protected as "Kawasan Lindung." The outermost transition area of GSK-BB contains agricultural fields of smallholders and some oil palm plantations.

This study was conducted in the Bukit Batu area (BB) in the northeastern part of GSK-BB covering an area of approximately 800 km2 (Fig. 4.1, Fujita et al. 2016). BB is located at latitude 1° 17–28′ north and longitude 101° 42′–102° 00′ east,

Fig. 4.1 Location of ten plots inside Bukit Batu area. Five random points were selected in each plot to set camera traps

including Bukit Batu Wildlife Reserve and industrial tree plantations of Bukit Batu Hutan Alam (PT. BBHA) and Sekato Pratama Makmur (PT. SPM). All of the study area comprises 4–8 meter-thick peat (Simbolon et al. 2011), with a water level of about 40–100 cm below ground (Kozan 2016).

The annual amount of rainfall in the study area is highly variable and its seasonality was unclear. Annual rainfall at Pekanbaru, located 100 km southwest of BB, ranged from 102 mm to 284 mm from 1999 to 2001, but was 1074–2596 mm from 2003 to 2011 (National Climatic Data Center 2012). Clear dry periods existed in some years, but the timing of such dry periods varied every year. Temperature ranged from 23 °C to 32 °C throughout the year.

The core zone in our study area was mostly covered by natural peat swamp forest. However, large trees within a few kilometers of both banks along the lower part of the Bukit Batu River in the Wildlife Reserve were logged by a logging company and smallholders during 1990s and 2000s (Watanabe et al. 2016). In the Wildlife Reserve, Gunawan et al. (2012) established two 0.5-hectare plots and measured all trees with diameter at breast height (DBH) > 3 cm. In the protected area, Partomihadjo et al. (2011) established two 1-ha plots and measured all trees with DBH > 10 cm. Tree species composition in these four plots were similar, the common major species being Palaquim sumatrana/dasyphyllum, Syszigium paludosa, Diospyros hermaphroditica, Mezzetia parvifolia, and Madhuca motleyana. The forest was categorized as mixed swamp forest following the classification by Anderson (1976).

The buffer zone comprises planted forest of A. crassicarpa with networks of canals for the water management and transportation constructed at intervals of 500–3000 m. A. crassicarpa is a fast-growing species well adapted to peat soil. It grows in dense stands within 3 years of planting. The two plantations in our study area were established during 1999 (Watanabe et al. 2016) and developed until 2006. Acacia trees are harvested and replanted on a 5–6-year rotation, and these planted acacia forests consist of patches of even-aged trees. The forest floor of the planted acacia forest is dominated by Dicranopteris sp. and Blechnum sp.

Hunting pressure on medium to large mammals in this area is considered to be low because there are no residents inside the study area. Also, most of the nearby inhabitants are Muslim (Suzuki et al. 2016) who rarely hunt wild mammals. Nevertheless, we found snares to catch wild boar set by outsiders and the skull of a sambar deer (Rusa unicolor) at a camp used by poachers in the Wildlife Reserve. Cause of death of the sambar deer—either at the hands of poachers or natural causes—was unclear.

#### 4.2.2 Camera Trapping

To evaluate the species composition and abundance of ground-dwelling mammals and birds, we deployed camera traps—automatic digital cameras with infrared motion sensors (Bushnell Trophy Cam, Model 119435, Bushnell, Olathe, KS), in BB, from December 2010 to October 2011.

We established four plots in the natural peat swamp forest inside the Bukit Batu Wildlife Reserve (WR1, WR2, WR3, WR4) and three plots in the natural peat swamp forest inside the protected areas of the two industrial tree plantations (PA1, PA2, PA3) (Fig. 4.1 and Table 4.1). While WR4 and PA1–PA3 were in primary forests, WR1–WR3 were in forests degraded by previous logging activities. WR1– WR4 were located about 500–1000 m from the banks of Batu River. The altitudes of WR1–WR3 were lower than those of all the other plots. PA1–PA3 were located away from the river, but located close to canals (approximately 500–1000 m) in the plantations. We also established three plots in the planted forests of A. crassicarpa inside the two plantations (AF1, AF2, AF3). They are all close to canals (approximately 500 m). Each of the ten plots covered a circular area with a radius of 500 m. The altitudes of these plots ranged from 8 m to 40 m above sea level. The forest floor was generally flat and covered in wooden debris. Ground surface of the plots were not flooded during the study period.

We set camera traps at five random points in each plot and compared the species compositions captured at each plot and a mean trapping rate (MTR) for each species.

Table 4.1 Longitude and latitude of centers of the ten plots

The camera was mounted onto a tree at a height of 50–100 cm above ground. We directed the camera to focus downward to capture a field-of-view of approximately 2–7 m2 . We set the camera to record in video mode for a duration of 10 s upon trigger. Once triggered, a camera could not be triggered again for 10 s. The camera was inspected every 3–5 months to change batteries and memory card.

All medium to large animals including mammals, terrestrial birds, and monitor lizard species captured by the camera traps were recorded and identified to species level based on Francis (2008), MacKinnon and Phillipps (1993), and Payne and Francis (2005). We classified wild boar as a morph-species (Sus sp.) because clear distinctions could not be made between Sus scrofa and S. barbatus, and some resembled a crossbreed between the two species. We defined a record as an independent record if the same species was not recorded during the previous 30 min. We counted the number of independent records of each species and the total length of camera-working days at each set point.

Conventionally, the trapping rate is the total number of independent records per total number of camera-working days multiplied by 100, and is termed as Relative Abundance Index (RAI) by O'Brien et al. (2003). As explained by Rowcliffe et al. (2008, 2013) and Nakashima et al. (2018), RAI can provide a index of animal density if the captured areas of a camera trap, animal speeds, and lengths of animal activity time in a day on average are not different among the plots. However, this value can be biased by the difference of camera-working days among the set points inside a plot. To offset the bias, we calculated a mean trapping rate (MTR) as an index of animal density as follows: a single-liner regression equation between the length of camera-working days at each set point and the number of independent records, with the regression line fixed to cross the point of origin. We multiplied the coefficient by 100 and defined the value as the MTR of the plot.

#### 4.2.3 Effects of Planted Acacia Plantations

We evaluated the effects of vegetation differences caused by conversion to planted acacia forest on the trapping rate of each species in BB using the model selection method. We also compared the similarity of species composition between plots in different vegetation types using nonmetric dimensional scaling (nMDS).

To evaluate the effect of vegetation differences, we made two nested mixed Poisson models (mixed with a fixed effect and a random effect) for a number of records or number of species captured at each camera set point. The equations of the two models are as follows:

> ð Þ þ ð Þ Model 1 N = wd þ P þ V Model 2 N = wd P,

where 'N' denotes the number of records, 'wd' is the length of the camera-working days, 'P' and 'V' are categorical data, representing the Plot ID and the vegetation type of the plot respectively. We compared goodness-of-fit of Model 1 and Model 2 based on Akaike's Information Criterion (AIC) values (Akaike 1973). AIC is a measure of goodness-of-fit with an added penalty for model complexity as measured by the number of fit parameters in the model (Burnham and Anderson 2002). We selected model with vegetation (model 1) as the best model when AIC of Model 1 was smaller than AIC of Model 2 and the difference was more than two.

On the other hand, nMDS is a robust unconstrained ordination method suited to community data that searches iteratively for best configuration of the plots from the dissimilarities (Gotelli and Ellison 2004). We plotted the ten plots following Bray-Curtis dissimilarity of species composition (the record numbers of each species) to investigate effect of the vegetation differences.

The generation of random set points for the cameras, and all statistical analysis were conducted using the statistical software R 3.0.1. (R Development Core Team 2013) with the packages of spatstat, lme4, and vegan.

#### 4.3 Results

#### 4.3.1 Faunal Composition in the Peat Swamp Forest of BB

Total camera-working days were 3978 days at 20 camera setting points in the four plots in the natural forest inside the Wildlife Reserves; 3336 days at 15 points in the three plots in the natural forest inside the protected areas of the HTIs; and 3675 days at 15 points in the three plots in the planted acacia forest inside the HTIs.

During the 10,989 camera-working days at 50 points in total, we obtained 1856 records of 19 species of mammals, 3 terrestrial birds, and 1 monitor lizard (Table 4.2). These included 11 vulnerable or endangered species on the IUCN Red

Table 4.2Mean trapping rate of each species

Table 4.2 (continued) (IUCN 2012). Main food habitat is based on Matsubayashi et al. (2007), Smythies (1999), and Myers (2009)

cacarnivore, hf herbivoreaΔAIC>2

 and frugivore, in

insectivore, om

omnivore

List (IUCN 2012), such as sun bear, Sunda clouded leopard (Neofelis diardi), marbled cat (Paradofelis marmorata), and Sunda pangolin (Manis javanica). The images of all captured species were presented in Fujita et al. (2012).

In addition to the species we recorded by camera traps, we were informed by local people that sambar deer and Sumatran tiger (Panthera tigris) also inhabited the natural peat swamp forest in this area. We found the skull of a male sambar deer at an illegal bird-hunting camp in the core area. It was also reported that a resident was attacked and killed by a tiger in the transition area.

The sum of MTRs of all species ranged from 9.22–51.85 (mean: 29.16), 8.75–31.76 (mean: 16.42), and 2.29–6.38 (mean: 4.02) in the natural forest inside the Wildlife Reserves, the natural forest inside the protected areas of HTIs, and the planted acacia forest inside the HTIs respectively. Camera-trap images were dominated by three species (Table 4.2), namely wild boar (Sus sp.), southern pig-tailed macaques, and lesser mouse-deer (Tragulus kanchil). Wild boar contributed to 43.2%, 69.0%, and 50.7% of all records in the Wildlife Reserves, the protected area of HTI, and the planted acacia forest, respectively. The southern pig-tailed macaques contributed to 16.9%, 19.9%, and 24.5%, respectively. Lesser mousedeer, which were abundant only in the Wildlife Reserve, represented 33.7% of all the images. All other species had low MTRs compared to these three dominant species.

Among the three terrestrial bird species we detected, crestless fireback (Lophura erythrophthalma) was the dominant species in the natural peat swamp forest, accounting for 82.1% of all the records, while red jungle fowl (Gallus gallus) was recorded only in the planted acacia forests.

## 4.3.2 Differences of Faunal Composition Between Natural Peat Swamp Forests and Planted Acacia Forests

The natural peat swamp forests had a higher species richness than the planted acacia forests (Fig. 4.2). The average number of species per plot and the range were 9.75 (9–11) in the Wildlife Reserve, 11 (11) in the protected areas of HTIs, and 5.3 (4–6) in the planted acacia forests inside the HTIs (Table 4.2). Total number of recorded species was 16 in the Wildlife Reserve and 18 in the protected areas of HTIs, and seven in the planted acacia forests inside the HTIs. The higher species richness in natural peat swamp forest was the result of the number of rare species. Among the 11 vulnerable or endangered species recorded in this study, eight were recorded in the natural forests, while the other three were recorded in both vegetation types. Of the 11 vulnerable or endangered species, none were recorded only in the acacia forest.

The best mixed models selected using AIC for MTRs of 12 species including all of the abundant species in our study area were with the vegetation variable, comprising natural peat swamp forest or acacia forest (Table 4.2). Among the 12 species, the MTRs of nine species were higher in natural forest than that of the planted acacia

forest. On the contrary, the MTRs of Malay civet (Viverra tangalunga), leopard cat (Prionailurus bengalensis), and red jungle fowl were higher in planted acacia forest than those in the natural peat swamp forest. Besides, the best mixed model with vegetation variable between the natural peat swamp forests of the Wildlife Reserve and the protected area of HTI, was selected for only three species. Lesser mouse-deer and crestless fireback were more abundant in the Wildlife Reserve, whereas shorttailed mongoose (Herpestes brachyrus) was more abundant in the protected area.

The result of nMDS also showed the similarity of species composition at plots in the Wildlife Reserve and protected areas of HTI, and the dissimilarity from those at plots in the planted acacia forest (Fig. 4.3, stress: 0.084). The four plots in Wildlife Reserve and three plots in protected areas of HTI are overlapping especially on Axis 1, while three plots in the planted acacia forest were plotted apart from them.

#### 4.4 Discussion

## 4.4.1 Ground-Dwelling Mammals and Bird Communities in Peat Swamp Forests

Our results indicated that the natural peat swamp forest in BB is a habitat for various mammal and terrestrial bird species including the vulnerable or endangered species. Notably, wild boar, southern pig-tailed macaques, and lesser mouse-deer were dominant species, accounting for 93% of all records (Table 4.3). One of the authors (HS) also conducted a camera-trapping survey in two lowland forests in East Kalimantan (Ratah Timber and Roda Mas) and one lowland forest in Sarawak (Anap-Muput) (Jati et al. 2018; Samejima and Hon 2019). Species compositions of the four study sites were similar, and wild boar (Sus barbatus), pig-tailed macaques (Macaca nemestrina), and mouse-deer (Tragulus kanchil and T. napu) were the top three or four most frequently recorded species also in the lowland

Fig. 4.3 Similarity of species composition among the ten plots shown in nonmetric dimensional scaling

a Samejima and Hon (2019) b

Jati et al. (2018)

forests (except Tragulus spp. in Anap-Muput). The proportions of wild boar found in the other sites were lower than those found in this study. While species accumulation curves in natural forests in this study were not yet saturated, species composition in peat swamp forest may be simpler than lowland rainforest in same region. Sasidhran et al. (2016) and Adila et al. (2017) also conducted a camera-trap survey in the North Selangor Peat Swamp Forest, Peninsular Malaysia, and recorded 4997 images of medium to large mammals. Among them, wild boar (Sus scrofa) accounted for 55% of all records. These results suggest that the natural peat swamp forest is an ecosystem dominated by wild boar. However, more studies in the natural peat swamp forest in this region are necessary to test these hypotheses.

## 4.4.2 Impact of Plantation Development Converted from Natural Peat Swamp Forests on Ground-Dwelling Mammals and Bird Communities

Our results indicate that the development of acacia plantations had a severe impact on species richness and abundances of ground-dwelling mammals and birds inhabiting the natural peat swamp forest. MTRs of most of the species, including the dominant species of wild boar, lesser mouse-deer and southern pig-tailed macaque, were lower in the planted acacia forests than in the natural peat swamp forests, suggesting significant population decrease of these species as a result of land-use conversion from natural peat swamp forests to industrial tree plantations. The obvious difference of species richness and the composition were also observed by Fujita et al. (2016) for bird species in same study area.

The plantation companies created substantial protected areas to maintain original vegetation. Our result showed that faunal diversity in the protected areas was as rich as in the Wildlife Reserve. However, faunal diversity inside the planted acacia forest itself was extremely low. As more than half of the species inhabiting the natural peat swamp forest were not detected in the planted acacia forests, the acacia forests are highly unsuitable as habitat for them. The planted acacia forest cannot function as a corridor for most of the ground-dwelling mammals and birds between the core area and the surrounding natural forests outside of GSK-BB.

McShea et al. (2009) also conducted a camera-trap study in an industrial tree plantation of Acacia mangium located in the lowland areas of central Sarawak in Borneo. In the plantation, many small patches of degraded natural forests were maintained among the planted acacia forests in addition to three large conservation zones. Presence of ground-dwelling mammal species in the remnant natural forests were higher than those in the planted acacia forests in general. Furthermore, mammal presence in the planted acacia forests were positively correlated with the closeness of the camera set points to the natural forests (McShea et al. 2009). The total number of species (n = 20) detected in the planted acacia forests was not much different (n = 24) than in the natural forests. McShea et al. concluded that planted acacia forest may not be an appropriate habitat for most endemic species, many species use the planted acacia forest to transit among patches of remnant natural forests. In short, the mosaic landscape of the acacia-natural forests may enable the persistence of many animal species in the industrial tree plantation. Nasi et al. (2009) also studied primates in an acacia plantation in lowland Sumatra, finding that natural forest fragments within industrial tree plantations do contribute to the maintenance of biodiversity. Further study may make it possible to propose appropriate design of such mosaic landscapes able to support original biodiversity as a feature of plantation development.

Some species such as Malay civet, leopard cat, and red jungle fowl may have adapted to the new plantation environment. McShea et al. (2009) also detected that wild boar (Sus barbatus), civet and mongoose (Herpestes brachyurus, H. semitorquatus, Paradoxurus hemaphroditus and Hemigalus derbyanus), and felidae (Pardofelis marmorata and Prionailurus bengalensis) were found in acacia forests more frequently than in logged natural forest. The adaptability of these species suggests that, whereas frugivorous species may have limited food resources in the planted acacia forest, carnivore species are not much affected.

Planted acacia forests in BB may also function as a buffer zone protecting intact natural forest in the core area from forest fires that frequently occur in the transition zone of BB (Eyes on the Forest 2013). The northern part of the transition zone in BB has burnt repeatedly and become an open area with few scattered-trees (Fig. 4.1). The plantation companies have monitored and prevented the spread of forest fires from the transition zone into their planted acacia forests.

A limited area of natural forest currently remains in lowland Sumatra. Our results indicate that natural peat swamp forest in the core area is vital to maintaining regional biodiversity, and more appropriate landscape designs, like acacia-natural forest mosaic, may boost biodiversity in industrial tree plantations.

Acknowledgments We greatly thank Mr. Canecio Munoz at Sinar Mas Forestry for his kind assistance and providing accommodation during our fieldwork. We also thank State Ministry of Research and Technology (RISTEK), the Ministry of Forestry, and Natural Resources Conservation Agency (BBKSDA), Riau, for permission to conduct this research.

We would like to express our heartfelt thanks to Alias Abdul Jalil, Yohannes Koto, Yuyu Arlan, Tju Kui Hua, Nevi Rasmika, Raffles Silaban, Zul Indra Fahmi, Edy Nazuardi, Hirimson Siahaan, Amir and other staffs from PT Sakato Pratama Makmur and PT Bukit Batu Hutani Alam, Dwi Hanum, Hutomo Rusiano, Eko, Sitinja, and Wahyu from BBKSDA, Haris Gunawan and Ridho Christina Siahaan from Riau University, Dicky from Pekanbaru, Idris, Abdul Gani, Khairin, and other field assistants from Desa Temiang, and Desa Parit I Api-Apito, who assisted in our fieldwork in Riau. We would like to also thank Yoshihiro Nakashima, who helped with discussion on cameratrapping methodology and helped to review this manuscript, and Kosuke Mizuno, who involved us in the research project in Riau, as project leader and organizer of this volume. We are also grateful to the anonymous reviewers for their constructive comments that improved our manuscript.

#### References

forest". The Indonesian Man and The Biosphere (MAB) UNESCO National Committee, pp 1–18

Smythies BE (1999) The birds of Borneo. Natural History Publications (Borneo), Kota Kinabalu

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# Chapter 5 Patterns of CO2 Emission from a Drained Peatland in Kampar Peninsula, Riau Province, Indonesia

Satyanto Krido Saptomo, Budi Indra Setiawan, Yudi Chadirin, Kazutoshi Osawa, Toshihide Nagano, Kosuke Mizuno, Dian Novarina, Susilo Sudarman, and Aulia Aruan

Abstract It was crucial to acquire soil CO2 flux data from a bare peatland site in Kampar Peninsula, Riau Province, Indonesia so as to evaluate the carbon budget of the site in which water is managed, drained, and utilized for acacia plantation. CO2 flux was continuously measured from July 2012 to February 2013 using an automatic soil CO2 flux measurement system. In this study, the factors affecting carbon emission were analyzed and tested for indirect CO2 flux estimation, and the results showed that CO2 flux varied with weather, water, and soil-related variables, and where there was rainfall, soil temperature and soil moisture both played an important role. CO2 flux was modeled using an artificial neural network (ANN) approach with inputs of soil moisture, temperature, and electrical conductivity (EC) as proxy variables. Based on the measurements, the total carbon dioxide (CO2) emission during the measurement period from July 2012 to June 2013 was 52.25 t ha-1 . Total CO2 emission in 2012 was estimated as 54.86 t ha-1 using the ANN model.

K. Osawa Utsunomiya University, Utsunomiya, Tochigi, Japan

T. Nagano Takasaki University of Health and Welfare, Takasaki, Gunma, Japan

#### K. Mizuno School of Environmental Science, University of Indonesia, Jakarta, Indonesia

Center for Southeast Asian Studies, Kyoto University, Kyoto, Japan

#### S. Sudarman PT Riau Andalan Pulp and Paper, Pelalawan, Riau, Indonesia

A. Aruan Alastair Fraser Forestry Foundation, Jakarta, Indonesia

S. K. Saptomo (✉) · B. I. Setiawan · Y. Chadirin

Department of Civil and Environmental Engineering, IPB University, Bogor, Indonesia e-mail<EMAIL_ADDRESS>
D. Novarina Asia Pacific Resources International Limited, Jakarta Pusat, Indonesia

Furthermore, the results generated by the model showed that levels of CO2 flux declined as the temperature decreased, and soil moisture increased toward soil water saturation.

Keywords MRV · Peatland · Water management · Artificial neural network

#### 5.1 Introduction

The development of peatland often involves permanent or temporary conversion from its natural condition into bare land. Bare peat will experience exposure to natural processes in the atmosphere and on the surface, which will affect wetting and drying and, later, will lead to peat decomposition, which releases CO2. Peat is organic soil that is composed of partially decayed vegetation and contains very little mineral content. Slow decomposition caused by anaerobic conditions is a necessary condition for its development (Belyea and Clymo 2001).

Plant remains are deposited onto the upper peat layer above the mean water table, and they experience aerobic decomposition while the remaining organic matter is buried in the saturated layer below the water table with minimal decomposition hence, water-table depth is key to the decomposition and accumulation of peat. The lowering of the water table will remove anaerobic constraints on decomposition and stimulate the loss of peat carbon to the atmosphere (Freeman et al. 2001). A lower water table will also increase the potential for forest fires and stimulate the loss of peat carbon through combustion (Page et al. 2011; Turetsky et al. 2011).

CO2 emission from peatland has been suspected as one of the major greenhouse gases that contribute to global warming and climate change. It is thought that this emission is dependent on the water table (Jauhiainen et al. 2008; Couwenberg et al. 2010; Jauhiainen et al. 2012; Nagano et al. 2012) and, consequently, drainage of peatland due to development and utilization will alter the emission levels of CO2. Hooijer et al. (2006) suggest that the annual amount of CO2 emission over peatland with a depth of 1 m of groundwater level is, on average, around 91 t ha-1 . However, this figure does not take into account the diurnal variation of CO2 flux emission over the peatland, its interaction with other environmental parameters, or peat bulk density.

Net CO2 emission can be measured using a flux tower, which requires investment in tower construction and sophisticated instruments. An indirect method using carbon budget analysis can be conducted to estimate whether the land is the source or sink of carbon emission. This requires information about carbon stored in all parts of trees and emission levels from the land. Therefore, soil emission should be continuously measured so as to acquire a CO2 emission diurnal data series throughout the period.

This study set up a monitoring station in an area that had been developed for forest plantation operations in Kampar Peninsula. As such, the land had been drained using canals following eco-hydro management, a water management scheme implemented on site. The water table is lowered by dropping the water level in the canals that were designed and built for water management purposes. The water table is designated between 40 cm and 90 cm below the soil surface.

This study aims to quantify CO2 emission from bare peat within the industrial forest area using continuous measurement; to determine environmental factors that affect carbon dioxide flux and develop an ANN model to estimate carbon dioxide flux with inputs of environmental factors. The measurement and analysis were conducted in the humid tropical area of Kampar Peninsula. The study focused on measuring and quantifying CO2 flux from bare peat within the Monitoring, Reporting, and Verification (MRV) station. The quantity of CO2 discussed in this paper is limited to CO2 flux from bare peat (CO2 efflux), and further use of the terms "CO2 emission" and "CO2 flux" will be limited to this.

#### 5.2 Methodology

Observations were made at a specially designated MRV monitoring station on the Kampar peninsula. At this location, the land surface has been cleared, and it consists of an open area bordered by a fence to protect it from disturbances during the observation process. The water level in this concession area is maintained at an altitude of 40–90 cm below the surface. For this study, weather, soil, and CO2 flux monitoring equipment was installed at the monitoring station.

The measurement of CO2 flux from the soil was carried out using a closed chamber method (LICOR 8100 IRGA gas analyzer) that can measure CO2 flux automatically based on a set time interval (Fig. 5.1). The dimension of the chamber was 20 cm in diameter and the time interval to close the chamber for data-reading was set to every 60 min (24 times per day). This was repeated three times, and the average CO2 flux was considered to be the measurement result. At the same time, the soil temperature, electrical conductivity, and volumetric water content at a depth of 5–10 cm were measured with another sensor (5TE) and recorded by a data logger (EM50). The daily CO2 emission was obtained by means of temporal integration

Fig. 5.1 Automatic soil CO2 measurement system Li-8100: analyzer (left) and chamber (right)

Fig. 5.2 Numerical integration to calculate daily CO2 emission

over the CO2 fluxes using the trapezoidal method. Then, the cumulative CO2 emission was calculated by summing up the daily CO2 emission for 365 days in order to obtain the total value for 1 year.

Measurement of CO2 emission from the soil was carried out using a closedcirculation chamber methods. In this method, the soil is covered with a chamber for a designated time, and gases that come out of the soil will accumulate within the chamber. The measurement of CO2 gas concentration was carried out by continuously flowing air from the chamber to the Infra-Red Gas Analyzer (IRGA) bench. Then, the analyzed air flowed back into the chamber. The measured change in CO2 concentration data was then processed into CO2 gas emitted from the ground. The whole process of measuring and processing data until the emission value was obtained was carried out automatically using the Li-8100 Automatic soil CO2 measurement system (Fig. 5.1). The emission data obtained in this measurement was a mixture of various physical, biological, and chemical processes in the soil, resulting in CO2 from the soil in the air per unit area.

The Li-8100 system includes a mechanical chamber and analyzer unit. The analyzer unit controls the mechanical CO2 chamber movement and air flow using its internal pump from the chamber to the IRGA sensor, analyzes CO2 concentration, calculates the CO2 emission, and stores the data in a detachable memory card. The measurement was set to be conducted hourly with three replications, and the repetition data was calculated in order to get an hourly average CO2 emission. At the end of the measurement, we produced an hourly data series for the measurement period.

The measured CO2 concentrations during one observation event were calculated automatically by the Li-8100 system and converted into CO2 flux with the unit of μmol m-2 s -1 . By multiplying the CO2 flux by its molar mass divided by 10-6 , the CO2 flux in units of g m-2 s -1 was obtained, after which the total emission for 1 day (d) was calculated using an integration of the hourly (h) emission curve (Fig. 5.2). This integration was calculated numerically using the trapezoidal area method (Eq. 5.1) in which the daily value was the sum of the hourly values over 24 h.

Fig. 5.3 ANN model to calculate CO2 flux

$$CO\_{2\ d} = \sum\_{h=1}^{24} \left( 0.5(CO\_{2\ h-1} + CO\_{2\ h}).3600 \right) \tag{5.1}$$

Thus, the total CO2 emission in 1 year can be calculated as follows.

$$CO\_{2\text{ y}} = \sum\_{d=1}^{368} CO\_{2\text{ d}} \tag{5.2}$$

Along with CO2, weather and soil parameters were also measured. These measurements were part of the MRV activity conducted on the site. The measured variables were hourly rainfall, solar radiation, air temperature, air humidity, soil temperature, soil electrical conductivity, soil moisture, soil water potential, and the water table. These variables were essential to describe the environmental conditions that would influence the soilwater status and CO2 emission from the soil. Occasional missing data of CO2 emission was filled by the predicted value using the ANN model. Furthermore, the linear equation was used to estimate the accumulation of CO2 emission within the missing periods.

As shown in Fig. 5.3, the ANN model consists of an input layer, a hidden layer and an output layer. The input layer had three variable inputs (soil temperature, electrical conductivity, and moisture), one bias input, and there was one variable output in the output layer, which was CO2 flux. The model that was previously developed by Setiawan et al. (2013) in other sites had achieved considerable results with the coefficient of deterministic (R2 ) being above 0.9. The related computer program (macro) was written in MS Excel, and the available Solver was used to find the optimized values of the weighted values. One reason to use these three variable inputs was knowing that there is a sensor available in the market that is capable of measuring these variables simultaneously. Once these relationships have been well proven, it is then possible to calculate CO2 flux at the same site from these three measured variables. For other sites, similar to this one, the model needs to be calibrated through the learning or optimization process.

#### 5.3 Results and Discussion

## 5.3.1 CO2 Dynamics

Raw data of soil emission measurement is shown in Fig. 5.4, which also shows soil temperature and soil moisture at the surface layer between 5 cm and 10 cm. Judging from the varying CO2 flux data, it can be seen that the carbon emission is influenced by environmental parameters. Figure 5.5 shows the variation of rainfall, soil moisture, and daily CO2 emission, on site. The red line is CO2 flux data measured with the Li-8100 system, and the green line is estimated data to fill the gap of missing data with a linear model. It is clear that CO2 emission also varies with weather conditions, i.e., rainfall, as well as water and soil-related variables.

Carbon emission was lowered during rainfall, which increased peat moisture and lowered temperature. The CO2 itself is produced during biological activities that are affected by moisture and temperature, and it can also be caused by human activities such as clearing and drainage of land. Thus, as soil moisture and temperature affect microbiological activity, their variation will contribute to the differences in CO2 emission in bare peatland.

Fig. 5.4 Measured soil temperature (Ts), volumetric water content (VWC), and CO2 flux

Fig. 5.5 Daily rainfall, soil volumetric water content, and CO2 emission

## 5.3.2 CO2 Correlation with Environmental Parameters

Soil CO2 flux is the natural result of complex dynamic processes that involve organic decomposition, as well as respiration of roots and microbes (Šimůnek and Suarez 1993; Luo and Zhou 2006) in which moisture content, chemical content, and soil temperature play important roles. Berglund et al. (2010) evaluated the lysimeter method (Persson and Bergström 1991) to study the effect of temperature on CO2 emission, which demonstrated the correlation of temperature to CO2 by using a general model for peatland that had been drained for purposes similar to this study.

Figure 5.6 shows the individual correlations between CO2 flux to air temperature (Ta), solar radiation (Rs), relative humidity (RH), soil temperature (Ts), soil electrical conductivity (EC), and soil volumetric water content (VWC). By looking to the low R2 , it is clear that CO2 flux is poorly correlated with this individual variable. However, we can see that CO2 flux tends to increase with the increase of Ta, Rs and Ts, and to decrease with the increase of RH, EC and VWC.

It is known that biological and chemical processes are dependent on temperature and energy. Water, on the other hand, has the opposite effect: Higher RH or VWC seemed to contribute to decreasing emission. It is known that CO2 will result as oxidation occurs, i.e., the land is being drained. Conversely, oxidation will cease as the land becomes saturated with water, subsequently releasing CH4. EC in the peat

Fig. 5.6 Individual correlations between CO2 flux with air temperature (Ta), solar radiation (Rs), relative humidity (RH), soil temperature (Ts), soil electricalconductivity (EC), and soil volumetric water content (VWC)

Fig. 5.7 Patterns of CO2 flux under various soil Ts and VWC at EC = 0.04 mS cm-1

can represent two parameters since the sensor actually detects conductivity, which may be due to an increase in chemical concentration or the conductive medium itself—water in the pores—and, thus, should be accounted for simultaneously with VWC. Heat and water strongly affect the emission, and two parameters that directly interact with biological processes in the soil are soil temperature and soil moisture. Therefore, these parameters can be a proxy in quantifying carbon emission.

Previously, Nagano et al. (2013) demonstrated the use of the groundwater table as a proxy parameter to estimate CO2 emission using an empirical three-stage model (NAIS Peat Model). This is used to describe the effect of a lowering groundwater table on CO2 release from peatlands. However, with highly porous material such as the peat in the study area and the water table at a depth of about 100 cm, capillary rise of water to the surface from groundwater is difficult to occur to wet the surface. Compared to the study area in Nagano et al. (2013), where the water table was less than 30 cm deep, it is reasonable to exclude the groundwater level from the model for the moment. In this case, CO2 emission may not be affected by groundwater level, as it had probably been managed at a particular level in the area.

During a prolonged dry period, even though the water table was well managed at about 100 cm below the surface, the peat on the surface suffered from the very dry conditions. In a rainfall event, peat VWC immediately increased, and CO2 flux dropped, as seen in the previous figures. These show that for the most direct estimation of CO2, VWC can represent the water status of the peat better than the water table if VWC measurement is available.

Figure 5.7 shows the flux estimated using the ANN model with inputs of Ts, VWC, and EC versus those measured by Li-8100 at the same time. ANN parameters and constant were optimized by using the measured data. The results of the CO2 flux calculation using the ANN model show an R2 value of 0.986, and the model is quite accurate when used for predictions of CO2 flux by observing proxy parameters of soil temperature, moisture, and EC. The ANN CO2 model seems to be able to represent the characteristic of flux as dependent on soil moisture, temperature, and EC, as seen in Fig. 5.7, which is the particular example at soil EC = 0.04 mS cm-1 .

CO2 flux increases with the increase in temperature; soil humidity, however, has a different impact on the flux. In Fig. 5.7, at any level of temperature, the flux reaches its maximum at soil moisture of between 0.31 cm3 cm-3 and 0.33 cm3 cm-3 . The flux decreases as soil humidity falls to a drier level or increases toward saturation. It can be concluded that either dry conditions or a saturation effect lowers emission. Dry conditions, however, will lead to soil subsidence and increase the potential for a peat fire, so wet conditions are preferable.

## 5.3.3 Accumulation of CO2 Emission

Figure 5.8 shows the daily accumulated CO2 emission measured in Kampar Peninsula from July 2012 to March 2013. The daily emission accumulated is shown by a solid rising line to the end of the available data. Measurement failures during the periods caused missing data, which were then estimated by using linear interpolation between dates with available data.

The total CO2 emission throughout the measurement period is shown by the ascending line in Fig. 5.8. If this line is extended to an entire 1-year period, using this simple approach, a 1-year accumulation of CO2 can be estimated at 5225 g m-2 (52.25 t ha-1 ). However, this approach ignores the effect of varying soil temperature, moisture, and EC, as presented in previous section and ANN CO2 model.

The ANN model for CO2 emission estimation can be used when direct measurement of CO2 data is not available. This can be done with a careful calibration procedure that includes direct measurements of CO2 flux, which provide sufficient data. In this case, calibration had been conducted at the MRV station in Estate Meranti, and the model can be used to predict the CO2 emission from the bare peatland. Figure 5.9 shows the emission series by using the ANN model, which shows that, in 2012, the total emission can be estimated as 5488 g m-2 (54.86 t ha-1 ).

Fig. 5.8 CO2 emission and accumulation

Fig. 5.9 CO2 emission estimated using the ANN model for 2012

Long-term field data of CO2 flux, including net ecosystem CO2 exchange, ecosystem respiration, and soil CO2 efflux to examine their variations for conditions of peat swamp forest in a tropical peat area in Central Kalimantan, Indonesia was presented by Hirano et al. (2016). Similar to Nagano et al. (2013), groundwater level is suggested as a practical measure to assess annual net ecosystem CO2 exchange.

Among the results presented, the annual sum of soil CO2 efflux or soil respiration for 2005 were 1347 g m2 year-1 , 1225 g m2 year-1 and 348 g m2 year-1 , at the undrained, drained, and drained and burnt peat swamp forest. This is equal to 4939 g m2 year-1 , 4492 g m2 year-1 and 1276 g m2 year-1 of CO2, which is smaller than the estimation that has been made in this study. The data on the variation of the mean groundwater level presented at the site shows that the condition of the groundwater level presented by Hirano et al. (2016) is relatively shallower compared to the site in this study, which may lead to a lower CO2 flux than that presented here. However, information on temperature, humidity and soil EC, and hourly measurement results are not available. Further studies on monitoring soil parameters, weather, measuring CO2 emissions, and developing ANN models at these locations and other locations will be of interest in developing indirect CO2 estimation methods.

#### 5.4 Conclusion

Continuous measurement of CO2 was conducted from July 2012 to February 2013, acquiring a CO2 emission data series (except for missing data) during this period. Measurement showed that CO2 flux emission varied with weather, water, and soilrelated variables, especially rainfall, soil temperature, and humidity. Using linear estimation, the total estimated CO2 emission during the measurement period from July 2012 to June 2013 is 52.25 t ha -1 . CO2 emission can be estimated using the ANN approach developed in this study, with inputs of soil moisture, temperature, and EC as proxy parameters; these can be applied for rapid estimation of CO2 emission from bare peat where it was calibrated. Total CO2 emission in the year 2012 is estimated as 54.86 t ha-1 using the ANN model, which also shows that CO2 emission fell with the decrease in temperature and increase in soil humidity toward water saturation.

Acknowledgements The authors would like to thank the Ministry of Environment and Forestry of the Republic of Indonesia for the permission given to evaluate the MRV program in the studied location from 2009 to 2014 and to express our appreciation for the support and assistance given during field surveys and throughout the program by Riau Andalan Pulp and Paper.

#### References

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# Part II Resilience and Adaptability of Peat Swamp Forest

# Chapter 6 Termite: Friend or Foe? Conservation Values of Termites in Tropical Peat Systems

Kok-Boon Neoh, Ahmad Muhammad, Masayuki Itoh, and Osamu Kozan

Abstract Termites are the major ecosystem service providers and contribute significantly to soil processes and nutrient cycling in tropical ecosystems. The ecological services provided by termites are often discredited due to their commonlyregarded status as pest in human-dominated landscapes, however. In order to understand the potential roles of termites in peatland ecosystems, termite samplings were conducted in abandoned degraded peatland and peatland cultivated with oil palm in Riau, Sumatra. Surveys found a total of six species of termite of the family Rhinotermitidae. (rhinotermitid) in study plots of disturbed lands. In particular, Coptotermes spp. are notorious pests to oil palm, and may also be a potential pest in indigenous tree replanting programs. Based on analysis of termite feeding groups and documentation of wood susceptibility to termite attack, this study provides a reference of tree species that must be avoided in indigenous tree replanting programs so that the trophic relations of termite populations are of most benefit to peatland soil biodiversity and thereby to resilient peatland ecosystems.

Keywords Pest · Termite damage · Wood susceptibility · Ecosystem service · Oil palm

K.-B. Neoh (✉)

A. Muhammad University of Riau, Pekanbaru, Riau, Indonesia

M. Itoh School of Human Science and Environment, University of Hyogo, Himeji, Hyogo, Japan

O. Kozan Center for Southeast Asian Studies, Kyoto University, Kyoto, Japan

Department of Entomology, National Chung Hsing University, Taichung, Taiwan e-mail<EMAIL_ADDRESS>
#### 6.1 Introduction

Insects contribute significantly to vital ecological functions such as pollination, decomposition, maintenance of wildlife species, and as biological control agent for crop pests (Losey and Vaughan 2006). At the same time, insects can be pests. If they appear at the wrong place or time they can threaten economic wellbeing and human health. This is particularly true when their natural habitat is disturbed by urbanization or other landscape modification, and when they are mismanaged.

Termites are major ecosystem service providers and contribute significantly in soil processes and nutrient cycling in tropical ecosystem (Holt and Lepage 2000). This vital role in ecological processes is often underacknowledged, as 5% of the 2300 termite species in the world are pests known to have negative economic impact on human settlements or agriculture (Su and Scheffrahn 2000). Recent increases in land-use intensity have elevated termites' pest status.

The Indo-Malayan region contains 62% of global tropical peatlands (Page et al. 2006). These peatlands have long served as a biodiversity hotspot for many endemic species of flora, fauna, and microbes (Yule 2010). Peatland ecosystems have also supported local community livelihoods based on long-term coexistence with peatland forests. Political policy, urbanization, and agricultural intensification, which came on the heels of increased global demand for food and fuel, resulted in the clearing of 80% of Southeast Asian peat swamp forest to make way for agroindustrial plantations (Mishra et al. 2021). In the early 2000s, 6% of tropical peatlands in the Indo-Malayan region was converted to oil-palm plantations (Koh et al. 2011), as abandoned secondary peatland increased 15% since the 1990s (Miettinen and Liew 2010). Both processes of land modification pose considerable risks to biodiversity. Even such little-regarded species as earthworms, which make similar contributions to soil processes as do termites, are disturbance-sensitive (Blanchart and Julka 1997). Owing to the unusual peat ecosystem that is highly acidic, anaerobic, and sensitive to fire and disturbed conditions, earthworms are nearly absent from disturbed peat (Cotton and Curry 1982). Termites, which tend to be much more resilient to land disturbance than are earthworms and other related species, could therefore be seen as a major soil engineer if their services could be harnessed efficiently.

As a first step toward understanding the potential roles of termites in the peat system, this study conducted termite samplings in abandoned degraded peatland and peatland cultivated with oil palm in Riau, Sumatra. The authors also combined the dataset of previous reports in disturbed and peatlands cultivated with oil palm. Based on analysis of termite feeding groups and documentation of wood susceptibility to termite attack, this study provides a reference of tree species that must be avoided in indigenous tree replanting programs so that the trophic relations of termite populations are of most benefit to peatland soil biodiversity and thereby to resilient peatland ecosystems.

#### 6.2 Materials and Methods

An abandoned degraded peatland site and a peatland currently cultivated with 5–6 year old oil palm were selected for this study (Fig. 6.1). The sites are located in the transition zone of the GiamSiak Kecil–Bukit Batu Biosphere Reserve (0°44′– 1°11′N and 0°11′–102°10′E) lying between 0 and 50 m above sea level. The study sites were 2 km apart, allowing the authors to exclude any major discrepancy in hydrological and climatic patterns, with the principal exception of canopy openness. The canopy cover in oil palm plantation registered an average of 37.8% ± 14.1 (±SE); in contrast, the abandoned degraded peatland was open shrubland.

Termite samplings were carried out in November 2012 using a standardized belt transect as described by Jones and Eggleton (2000). The belt transect comprised a survey area of 100 × 2 m and was divided into 20 sections of 5 × 2 m. A collector spent an hour on each section to collect the soldiers and worker termites from termite potential habitats such as dead tree branches, tree logs, soil under logs, termite galleries and nests. Collected termites were stored in 80% ethanol until identified. The termites were sorted to species level based on identification keys on the termite fauna in the Indo-Malayan region (Thapa 1977; Tho 1992; Gathorne-Hardy 2004).

Termite species richness was estimated using Chao 2, Incidence-based coverage estimator (ICE) and Jackknife 1, and the diversity indices (Shannon and Simpson Indices) were generated using EstimateS Version 8.2 (Colwell 2009). The relative abundance of termites was generated based on the encounter rates of termite over 20 sections. The relative abundance in abandoned land is relative to the value in oil palm cultivated land transect (relative abundance = 1).

Fig. 6.1 Abandoned degraded peatland (left) and peatland cultivated with 5–6 year-old oil palm (right) were selected as study sites. Both landscapes were dominant in transitional area of the GiamSiak Kecil-Bukit Batu Biosphere Reserve

#### 6.3 Results

The observed species richness accumulation curve for both study sites reached an obvious asymptote close to estimated species richness curves (i.e., Chao 2, Incidence-based coverage estimator and Jackknife 1) (Fig. 6.2). In addition, all termite species were consistently encountered throughout the sampling as evidenced from no termite species only found once or twice (Singletons = 0; Doubletons = 0).

A total of six species of termite, which comprised a single family Rhinotermitidae, two subfamilies (i.e., Rhinotermitinae and Coptotermitinae) and

Fig. 6.2 Species accumulation curves of termites sampled in abandoned degraded peatland and peatland cultivated with oil palm in Riau, Indonesia. Species observed is the number of termites that were collected in the 100 m belt transect of 20 sections, and the numbers of termite species that were beyond observation were predicted using Chao 2, ICE and Jackknife 1

three genera (i.e., Coptotermes, Schedorhinotermes, and Parrhinotermes) were collected in both study sites. Coptotermes gestroi (Wasmann) was found only in abandoned degraded peatland, but Parrhinotermes pygmaues (John) and Schedorhinotermes malaccensis (Holmgren) were absent compared to that termite assemblage in degraded peat land cultivated with oil palm (Table 6.1).

Overall, the relative abundance of termites in abandoned degraded peatland was 65% of that in degraded peatland cultivated with oil palm (Fig. 6.3). In the abandoned degraded peat Coptotermes kalshoveni (Kemner), Parrhinotermes aequalis (Havilandi) and Schedorhinotermes medioobscurus (Holmgren) were predominately found (Shannonexp index: 3.34; Simpson index: 3.45), with relative abundances of 23%, 41%, and 32%, respectively. In contrast, the termite dominance in oil palm is even (Shannonexp index: 4.93; Simpson index: 5.50). Each species registered approximately 20% of encounter rate in the transect sampling (Table 6.2). All termite species found were wood feeders and wood-nesting termites.

#### 6.4 Discussion

The termite assemblage in the transition zones of the GiamSiak Kecil-Bukit Batu Biosphere Reserve contained only a single family of lower termite–Rhinotermitidae (rhinotermitids), which consist of two subfamilies (Coptotermitinae and Rhinotermitinae), three genera, and six termite species. The result accords closely with the termite assemblage sampled in newly cleared land (Vaessen et al. 2011). The low termite-species richness in the present study sites might be the result of land disturbance during the clearing process or repeated fires, causing the collapse of susceptible termite species. Only wood-nesting termites survived at post disturbance (Neoh et al. 2016). However, this result needs to be viewed with caution, as it was based only on a single landscape. A study at a 2 year-old oil plantation conversion from mixed swamp forest in Sri Aman District, Sarawak, documented two subfamilies of higher termites (i.e., Nasutitermitinae and Termitinae) and a subfamily of lower termite (Rhinotermitinae) (Vaessen et al. 2011). Another study of 5–7 year-old and 13–15 year-old oil palm plantation sites recorded more than 12 species, which comprised two subfamilies of lower termites (Coptotermitinae and Rhinotermitinae), and four subfamilies of higher termites (Nasutermitinae, Termitinae, Amitermitinae, and Macrotermitinae [only found in 15–17 year-old plot]). These findings indicate that the assemblage of termites shifts from a single dominant family to multiple families (i.e., rotten-woody feeder and soil feeder) along with the age of plantation. Kon et al. (2012) attributed this phenomenon to the improvement of soil condition across time, which favored the termite colony and territory expansion. In addition, incremental increases of dead wood, woody-plant basal area, and canopy height (as the plantation aged) may also be associated with the presence of multiple woody and soil feeders taxa in disturbed areas (Jones et al. 2003).

Termite species richness and relative abundance typically decline following logging and land-clearing activity (Jones et al. 2003). The lack of termite assemblage

Listoftermitespeciesfoundinabandoneddegradedpeatlandandpeatlandscultivatedwithoilpalmatdifferent

Table 6.1 (continued)

Emicroepiphyte-feeder

aClassification of feeding group based on Donovan et al. (2001)

bPresent study in Bukit Batu transitional areas

cDatasourcesfromVaessenetal.(2011)andKon et al.

 (2012)

> dDatasourcefromCheng etal.(2008)

 eData source from Faszly et al. (2011) and Jalaludin et al. (2018)

Fig. 6.3 Relative abundance of termite sampled in abandoned degraded peatland and peatland cultivated with oil palm in Riau, Indonesia. Relative abundance in abandoned land is relative to the value in oil palm cultivated land transect (relative abundance = 1)

Table 6.2 Termite species richness and diversity indices for degraded peat land and degraded peatland cultivated with oil palm

in burned peatland may have reduced the functional groups of termites that are essential to soil processes. Okwakol (2000), who studied land conversion from natural forest to banana-cultivated land, has suggested that a drastic reduction of 40% of the original termite diversity potentially alters ecological processes and reduces farmland productivity over time. In the present study, termite species richness fell to one family, Rhinotermitidae. It is still difficult to estimate what rates of termite abundance and diversity can elicit significant ecosystem function in a given area. However, termite groups with proven ecosystem functionality (e.g., Macrotermitinae [Beaudrot et al. 2011], Nasutitermitinae [Jiménez et al. 2008]) were not found in our study sites. Several management practices are therefore required to maintain termite diversity when peatland is disturbed. These management techniques include: (a) using low-impact land-clearing techniques and leaving tree residues in the disturbed area (rather than burning them) in order to maintain termite populations (Bourguignon et al. 2018); and (b) maintaining patches of intact forest within a disturbed area (Neoh et al. 2017). These methods would promote the survival of termite species vulnerable to land disturbance and help restore termite biodiversity in degraded lands (Jones et al. 2003).

In mineral soils, termites are known to make subterranean tunnels that improve soil water infiltration rates, while their role in decomposing plant litter and mound building via soil translocation enriches soil nutrients and improves soil bulk density, respectively (Holt and Lepage 2000). In the Indo-Malayan region, the termite assemblage in peatlands is mainly comprised of decayed-wood feeders and soil feeders (Table 6.1). Black and Okwakol (1997) have noted that wood and soil feeders generally contain high levels of organic carbon in their nests; the latter's nest, in particular, is rich in nitrogen due to their habit of feeding on highly decaying soil-like cellulose materials. A study on soil-feeding termites in the Colombian savanna revealed that the group was reputedly more important in soil nutrient enrichment than ants (Jiménez et al. 2008). Studies on soil impact by Nasutitermes sp. revealed that termite mounds can act as source of nitrogen available to plants in nutrient-depleted savanna systems (López-Hernández 2001; Jiménez et al. 2006). The presence of other feeding groups in the peat system (Table 6.1) may also be vital in the optimal functioning of the ecosystem and peat restoration.

In savanna systems, grass-harvesting termites are believed to play an important role in fire management, especially when they remove grass and plant litter that could act as fuel during fire events (Davies et al. 2010). Though grass-harvesting termites are not found in the Indo-Malayan region, the presence of wood feeders of multiple taxa potentially reduces the availability of fuel materials in the peatlands.

Fire disturbance and land conversion in the peatlands under study have caused a drastic collapse in the termite assemblage, leaving only rhinotermitids behind. Although the termite assemblages reported by Vaessen et al. (2011) and Kon et al. (2012) were diverse, the relative abundance of rhinotermitids in a young oil palm plantation of 5–7 years-old was generally high compared those found of other families, as rhinotermitids account for more than 70% of the total termite encounter.

Coptotermes are highly destructive termite species in Southeast Asia. Studies conducted on oil palm growth in peatlands in peninsular Malaysia and Sarawak reported that Coptotermes curvinagthus (Holmgren) is a major pest that causes death in both mature and young oil palm trees (Kim Huan and Silek 2001; Cheng et al. 2008; Kon et al. 2012). Similarly, approximately 14% of rubber tree planted on peat soil were attacked by C. curvignathus (Indrayani et al. 2022). Rasmussen et al. (1982) suggested that an abundance of food sources from the incomplete removal of timber residue during forest clearing could account for the high prevalence of C. curvinagthus attack on new plantation plots. This premise has been discounted, however, as no clear relationship between the termite attack and plant residue was found in an oil palm plantation on mineral soil (Kirton et al. 1999), which could be the result of the dominance and resilience of C. curvignathus in newly cleared land (Cheng et al. 2008). Coptotermes sepangensis Krishna, C. borneensis Oshima, C. gestroi, and C. kalshoveni were also found in oil palm plantations, but the species generally nest in rotten wood and rarely cause tree death. Nevertheless, C. gestroi and C. kalshoveni are notorious for damaging building structures in the tropics (Kirton and Azmi 2005). Other termite species Schedorhinotermes spp., Globitermes globosus (Havilandi), and G. sulphurues (Havilandi) were occasional pests in oil palm plantations (Harris 1969). Their pest status may be elevated if their food sources and habitats are further disturbed.

Replanting fast-growing indigenous tree species in abandoned degraded peatland such as Dyera lowii, Tetramerista glabra, Palaquium sumatranum, Palaquium burckii, Cratoxylon arborescens, and Callophyllum lowii is favored in peat rehabilitation programs; it can also be a key element in local community activities and source of livelihood (Gunawan et al. 2011, 2013). Nevertheless, some of these tree species are susceptible to termite attack (Table 6.3). Thus we do not rule out potential termite attacks in community-planted forests, just as have occurred in oil palm plantations. To ensure the survival and growth of newly-planted trees, farmers must take into account species selection and wood resistance to termite attacks. Otherwise, sustainable pest management and preventive measures should be devised before termites become a serious threat to newly re-planted peatlands.

#### 6.5 Conclusion

In Southeast Asia, termites are generally thought of as pests, and their ecological services to farmlands is underappreciated. With little awareness of the potentially vital role in nutrient management these soil-dwelling insects could play, farmers have often applied chemical pesticides to control termite populations in peatland oil palm plantations. Farmers drench the soil with chemicals or directly spray infestations. In most cases, these methods are repeatedly applied, as termite infestations generally reoccur after the chemical effect dissipates.  It is interesting to note that farmers in Africa possess comprehensive knowledge of termite ecology, termite species, and utilize various traditional eco-friendly control practices (Sileshi et al. 2009). Our study demonstrates that sustainable pest management and preventive measures such as tree-selection programs should be devised before termites pose a serious threat to newly planted trees.

Table 6.3 Resistance of Indonesian natural/planted timbers to termite attack

(continued)

Table 6.3 (continued)

a Wood resistance to termite attack based on the weight loss of wood consequence upon termite attack set by Standard Nasional Indonesia (SNI 2006)

Acknowledgments We thank Mr. Nur who kindly provided accommodations during our eld fi survey. K.-B.N. was an international researcher fellow of the Japan Society for the Promotion of Science. This study was partly funded by Large-Scale Research Program "Promoting the Study of Sustainable Humanosphere in Southeast Asia" funded by the Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT), 2011–2016, and the institutional collaboration feasibility studies "Toward the Regeneration of Tropical Peatland Societies: Building International Research Network on Paludiculture and Sustainable Peatland Management", Research Institute for Humanity and Nature (RIHN).

#### Referecses

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# Chapter 7 The Timber Processing and Retail Sectors in Pekanbaru, Riau: Toward Reforestation by Local People

#### Haruka Suzuki

Abstract The integral role of the timber industry in sustaining local reforestation efforts is often overlooked. The processing and retail sectors of the timber industry are significant in that they add value to forest resources, providing local jobs and lumber supplies. This study examines these sectors in Riau, Indonesia, where deforestation and degraded peatlands have been accelerating. Based on a field survey of randomly selected molding mills and timber kiosks in Pekanbaru, the author examines the nature and scale of timber production, sales, and supply chains. Both the molding mills and timber kiosks could be classified into three types: those that function as subcontractors of large pulp and paper companies, those that operate independently and provide timber supplies for local construction needs, and those that specialize in higher-end wood processing. Those providing local timber supplies demonstrated more adaptability in terms of meeting market demands. Depending on the level of the operation's independence, timber supply chains were more diversified in terms of the relationships among loggers, distributors, and retailers. To develop the timber sector in the context of local reforestation, it is necessary to consider how local people connect with the local timber processing and retail sectors and how to improve local timber supply chains. Reforestation programs must construct sustainable management systems that restore forests while at the same time using forest resources. This includes developing land ownership and use systems to produce timber sustainably and replanting tree species that are useful as timber.

Keywords Tropical timber · Timber supply · Reforestation · Forest management · Riau · Indonesia

© The Author(s) 2023

H. Suzuki (✉)

Faculty of Humanities and Sciences, Kobe Gakuin University, Kobe, Japan e-mail<EMAIL_ADDRESS>
K. Mizuno et al. (eds.), Vulnerability and Transformation of Indonesian Peatlands, Global Environmental Studies, https://doi.org/10.1007/978-981-99-0906-3\_7

#### 7.1 Introduction

The eastern province of Riau in Indonesia has been experiencing an accelerated rate of deforestation and peatland degradation since the 1990s. Furthermore, since the early 2010s, economic and population growth has precipitated a rise in the demand for timber for building construction. Local reforestation efforts are expanding in many areas in Riau, particularly in the context of rehabilitating degraded peatlands. At the same time, the Indonesian government has been developing several reforestation programs with the aim to increase both local timber supplies and employment opportunities. For example, in parts of Java, the planting of Paraserianthes falcataria and Albizia chinensis has successfully benefited the local wood processing industry (van Noordwijk et al. 2007).

Timber supply networks in Java directly connect local timber producers and wood processing companies, stabilizing the timber supply and decreasing gaps in timber distribution (Iwanaga and Masuda 2012, pp. 42–47). Although numerous studies examine the question of "illegality" in the production of timber in Indonesia, particularly in the context of political and economic change in the country (see Obidzinski 2005, pp. 193–205; Casson and Obidzinski 2002, pp. 2133–2151), the question of how timber production and supply by local communities could support reforestation requires more investigation and discussion. This chapter examines the socioeconomic characteristics of the timber processing and retail sectors—which are important hubs of the local timber supply system that connect timber producers, suppliers, and consumers—and discusses these sectors in the context of reforestation by local communities.

The chapter focuses on Pekanbaru, the capital city of Riau. Although the use of concrete has increased in building construction in many areas of Indonesia, timber remains an essential building material. Therefore, timber demand is expected to continue to increase in Riau due to sustained economic and population growth (BPS Provinsi Riau 2019, 2022a, b, c, d). Pulp and paper production in Riau is also expected to increase, as Riau is a main area of pulp production in Indonesia and pulp production has rapidly increased in recent years (Kementerian Lingkungan Hidup dan Kehutanan 2019, 2020). How, then, can Riau fill these demands in an environmentally and economically viable way?

Timber has long been one of eastern Sumatra's primary marketable products and subsequently is a valuable commodity. Historical research on the timber industry in eastern Sumatra has paid attention to the socioeconomic aspects of logging and timber trading. For example, Barnard (1998) studied the timber trade in premodern Siak, a kingdom located on the east coast of Sumatra, from 1723 to 1946. Local communities living along the Siak River—which is the deepest waterway in the Malay Archipelago and thus an important conduit for trade in the region—exported timber, for which Siak was well known throughout the Straits of Malacca and as far away as Java (Barnard 1998). Pastor (1927) examined the logging practices of the ethnic Chinese in the coastal area of east Sumatra during the 1860s and 1870s. They formed groups of 20–30 people and logged while staying in small sheds in the forest. The timber was then exported to Singapore. By the nineteenth century, the importance of the Siak timber trade was cemented, as other regions of Southeast Asia, particularly those with easily accessible teak forests, became deforested (Barnard 1998). Indeed, ethnic Chinese logging groups continued to actively operate until 1962, when trade between eastern Sumatra and Singapore was controlled by regulation.

After the Suharto period, economic and political crises caused political power to shift from centralized, authoritarian rule to democratization and decentralization, impacting local timber business in ways that are discussed in the context of political ecology. In analyzing the networks of exchange and accommodation surrounding illegal logging, McCarthy (2006) found that in some local villages, people engage in illegal logging when the price of cash crops, such as nilam (patchouli oil), decreases and they need other sources of income. Yet while such practices offer short-term profit, illegal logging is understood to be inherently unsustainable, because the main income source, commodity production, remains only partially developed (Potter and Badcock 2004, pp. 341–356). Iwanaga et al. (2014) investigated how local timber production functions in the global carbon credit system, using the Melaleuca (Melaleuca cajupti) market in Indonesia as a case study of forest management and timber production in a REDD+ (reducing emissions from deforestation and forest degradation) project. Finding that demand for medium-sized Melaleuca timber increased, Iwanaga concludes that Melaleuca silviculture and timber production could be one option to increase local people's income in the REDD+ project (Iwanaga et al. 2014).

Previous studies such as these illustrate the integral role that the timber industry plays in sustaining local livelihoods and forests. To improve understanding of this role, this chapter clarifies the socioeconomic characteristics of local timber processing and retail operations, focusing on molding mills and timber kiosks in Pekanbaru, Riau. Molding mills are outfits that have a number of circular and band saws to process logs and lumber, but they do not operate sawmills, which process raw wood into logs. Timber kiosks simply sell processed timber and do not have any machines to process the timber.

In describing these operations, I also briefly explain the relationship between pulp and paper companies, which produce and process timber on a large scale, and local business, which produce and retail timber on a small scale. Finally, I conclude by noting some challenges and opportunities of the local timber industry vis-à-vis reforestation efforts by local people.

#### 7.2 Method

#### 7.2.1 Research Site

Research was conducted in Pekanbaru, Riau, Indonesia (see Figs. 7.1 and 7.2). Although very few logs are produced in Pekanbaru, sawn timber and plywood are

Fig. 7.1 Map of Riau Province, Indonesia

produced in the city, making it a timber processing and retail area of Riau. In addition, Riau is the center of the pulp and paper industry in Indonesia, with several major companies operating in the province.

Until several years ago, local molding mills and kiosks in Pekanbaru were mainly located in two areas. The first area, along the Siak River, was the main access point for transporting logs when logging was active along its tributaries.

The second area is in the southern part of Pekanbaru, where molding mills and timber kiosks line the major roads for local commodity distribution in Riau. These local timber businesses continue to be active, for example on the north-south road to Dumai, a mid-coastal city and the east-west road stretching from Tembilahan in Indragiri Hilir to Rengat in Indragiri Hulu.

#### 7.2.2 Field Survey and Interviews with Stakeholders

Official records and statistical data do not accurately reflect the timber operations in Pekanbaru, because some businesses engage in illegal practices as defined by current forestry policy.1 To address this gap in knowledge, a field survey was conducted in November 2011 and February 2012 of randomly selected molding mills and timber

1 This is based on an interview with the officer in the Department of Forest Production Utilization and Monitoring (Balai Pemantauan dan Pemanfaatan Hutan Produksi, BP2HP) in Pekanbaru, Riau, on October 28, 2011.

Fig. 7.2 Research area and major roads in Pekanbaru, Riau, Indonesia. The black lines are the roads

kiosks along the major roads in Pekanbaru. The author also interviewed the timber business managers and workers about their production scale, the timber species and sizes that they process and sell, their management practices, and supply chains, with a focus on the relationships among loggers, distributors, and retailers. The interviews provide useful information and perspectives for better understanding the local timber processing and retail sectors in Pekanbaru.

#### 7.3 Findings

#### 7.3.1 Local Molding Mills and Timber Kiosks

#### <IP_ADDRESS> Business Characteristics and Scale of Production and Sales

The molding mills are classified into three types based on their business characteristics, as shown in Table 7.1. The first type represents the majority of those in Pekanbaru and includes those molding mills that functioned as subcontractors of

large pulp and paper companies. They processed wooden pallets for the pulp and paper companies, which then used the pallets to export pulp to foreign countries. This type of molding mill had a comparatively large production volume compared to the other types, which processed timber for building construction and/or furniture. Many of these molding mills began pallet processing a few years prior to the survey. Several managers of these molding mills noted that one of the reasons they work with the pulp and paper companies is that it does not require them to obtain any permission to process and retail timber, because they can work under the permission of the larger companies. Asia Pulp & Paper Co. Ltd. (APP) and Asia Pacific Resources International Holding Limited (APRIL), major pulp and paper companies in Indonesia, plant and harvest Acacia in plantations and process the cut wood into pulp at their plants. Other than the pallets, the local molding mills and timber kiosks mainly sell timber, such as building materials, to the local communities. This division of labor in the supply chain eliminates competition between the pulp companies and local operations. However, some Type 1 mills develop a dependence on the pulp and paper companies.

The second type of molding mill refers to those that processed timber to sell to both local people and government entities for building construction. This included sales of timber to the local government, which had a number of ongoing infrastructure and public projects that required timber. The production scale of these mills depended on the scale of government projects.

The third type includes the molding mills that processed timber specifically for doors, window frames, and roof ornamentation. These mills produced less than 50 m3 of processed timber per month.

The business characteristics of the surveyed timber kiosks are classified into three categories in Table 7.2. The first type consists of timber kiosks that retail timber to the mills that are subcontracted by pulp and paper companies, local people, and local government. This type of kiosk sold more species and sizes of timber than the other two types of kiosks. Moreover, overall sales were higher in this kiosk than in the other types, with the amounts of timber product sold ranging from 50 m3 to 200 m3 per month.

The second type includes timber kiosks that retailed timber to local people and government for house and building construction. Their sales amounted to less than 150 m3 per month. The third category consists of kiosks that retailed timber for doors, window frames, and roof ornaments. Their sales amounted to less than 50 m3 per month.

#### <IP_ADDRESS> Managers' Ethnicities and Hometowns

Based on interviews with managers and workers, almost all the managers of both the molding mills and timber kiosks surveyed were born in Riau. Most managers were from Pekanbaru, but some were from Kampar and Siak, neighboring regencies to Pekanbaru. They were predominantly Melayu people, who are traditionally distributed along the east coast of Sumatra. Some Batak and Minangkabau managers were

born in North Sumatra and West Sumatra, but had been living in Pekanbaru for more than 10 years at the time of the study.

#### <IP_ADDRESS> Workers

Both the molding mills and timber kiosks were mainly family-operated businesses. As shown in Table 7.3, on average, two family members, for example, the manager's wife and his relative, worked in the molding mills and timber kiosks. In addition to this core management team, migrant workers were also common in the molding mills and timber kiosks. Based on interviews with managers, working in a molding mill or timber kiosk was a typical way for migrants to make a living in Riau. Almost all these workers were young men in their 20s and 30s, who came from North Sumatra, West Sumatra, and Java.

#### <IP_ADDRESS> Business Management

It was extremely difficult to obtain a clear picture of the business operations of molding mills and timber kiosks due to the persistent sensitivity around illegal logging. Many managers did not want to reveal details about their businesses. One molding mill, however, did share about its operations in an interview on October 22, 2011. The monthly income and expenses of this molding mill in October 2011 are presented in Table 7.4.

The Type 1 mill processed wooden pulp pallets for pulp and paper companies. It was managed by the owner; his wife and uncle assisted in management (their salaries are not included in Table 7.4). The owner employed 12 workers. Labor costs amounted to IDR 1,415,000 per month in total. This value exceeds IDR 1,200,000 (approximately USD 80), which is the average monthly wage for a timber processing worker in Indonesia (BPS 2012). He also had to pay annual land rental costs, as the business was operated on borrowed land.

Table 7.3 Background of workers in the surveyed molding mills and timber kiosks (Source of data: Author's field survey)

a Javanese, Minangkabau, Batak, and Melayu migrants came from Java, North Sumatra, and West Sumatra

Table 7.4 Monthly income and expenses of a Type 1 molding mill in October 2011 (Source of data: Author's field survey)

The mill's operating income was IDR 26,380,000 (approximately USD 1978). This value comprised approximately 21% of total sales. The owner said that the sales during this month were comparatively high because he had produced and sold timber to a public project. The mill sold a total of approximately 200 m3 of timber during the month.

#### 7.3.2 Timber Supplies in Molding Mills and Timber Kiosks

#### <IP_ADDRESS> Change in Species and Sizes of Logs and Timber

The research made clear that the species and sizes available in both molding mills and timber kiosks had changed in recent years. Table 7.5 details the 22 species available in the surveyed businesses. Based on interviews with managers, until a few years prior to the survey, Punah (Teramerista glabra Miq.), Kulim (Sorodocarpus borneesis), Meranti (Shorea spp.), and Tembesu (Fagraea spp.) were mainly sold for house construction, because these species are highly durable and not susceptible to insect damage. However, in recent years, the amount of logs of these species had decreased drastically. As a result, many molding mills and kiosks had difficulty buying these types of logs and timber. At the time of the survey, these species were

(continued)

aMeranti includes Meranti Merah, Meranti Kuning, and Meranti Batu

bTelas refers to fallen trees in the forest. The species retailed as Telas were Ubar (Eugenia spp. and Syzigium spp.), Kulim (Sorodocarpus borneensis), and Daru– Daru (unknown) already recognized as rare and were used only for wooden fittings. Their prices were also continually on the rise.

Conversely, Acacia (Acacia cracicarpa), Mahang (Macaranga spp.), Karet (Hevea brasiliebsis Muell. Arg), and Sengon (Paraserianthes falcataria) had replaced Punah, Tembesu, and Kulim as the main timber species for house construction. These species grow quickly and produce timber in a short time span. They are not planted to be cut, but rather grow naturally in forests. Generally, during hardwood timber shortages, demand for these species was expected to spike. Acacia, Mahang, and Sengon were also essential materials for wooden pallets. The molding mills working as subcontractors of the pulp and paper companies specialized in processing these species. The buying price of Acacia, Sengon, and Mahang from the timber distributors was IDR 300,000 to 350,000 per m3 (approximately USD 23–26). The buying price of timber for house construction ranged from IDR 1,000,000 to IDR 1,500,000 per m3 (approximately USD 75–113).

Some timber kiosks also adapted their operations due to the hardwood shortage, for example, by selling a wider variety of species and buying any timber large enough in size for house construction from loggers and middlemen. In contrast, some timber kiosks concentrated on selling only a few timber species. For example, several timber kiosks sold only Mahang for house foundations, or sold thin Mahang logs, which were easy to obtain and requires no processing (local people place Mahang piles on the muddy ground to prevent houses from sinking). Such specialization indicates an adaptation to local timber supply and demand.

Although the industry classifies 22 timber sizes, the length of a single piece is typically 400 cm. By the time of the survey, however, some kiosks had started to sell hardwood timber in lengths of 200 cm. This could indicate a further adaptation; by shortening the length, kiosks were able to continue selling hardwood. Some timber kiosks had also begun to sell lumber. Lumber was cut roughly by hand saw rather than processed by band or circle saws in sawmills and molding mills. Although the lumber's surface was not smooth, the longer length could be maintained, with kiosks offering sizes of 5 W × 3D × 400 L (cm) and 6 W × 4D × 400 L (cm). Some kiosk managers indicated that local people preferred larger-sized lumber pieces to smallersized timber pieces.

#### <IP_ADDRESS> Timber Supply

Based on interviews with managers, until several years ago, the molding mills and kiosks cooperated to share information such as timber distribution channels and prices, but no longer did so at the time of the survey due to the declining number of timber processing and retailing operations in Pekanbaru.

Distributors transported timber to the molding mills and timber kiosks either via the roads or the Kampar River. Table 7.6 depicts the sources of timber for the surveyed molding mills and timber kiosks according to producing district and forest type. The molding mills bought lumber produced in Siak, Kampar, and Pelawan. In Siak, the main areas of timber production were concession forests, which were

Table 7.6 Sources of timber in the surveyed molding mills and timber kiosks according to producing districts and forest type (Source of data: Aurthor's field survey)

managed by the pulp and paper companies or their subcontracted mills. In Kampar and Pelawan, village forests were the main areas of timber production.

The timber kiosks bought timber that was processed in Siak and Kampar. Although there were several molding mills in Pekanbaru, the timber kiosks did not buy timber from them, because many molding mills in Pekanbaru processed mainly softwoods for wooden pallets and did not meet their demands. Some kiosk owners said that village forests were the main areas of timber production in both Siak and Kampar. Others did not know where timber was cut.

The author found that at the time of the survey, 22 out of 25 of the molding mills and timber kiosks were facing timber shortages. First, the hardwood supply was declining, as described in the previous section. Second, no one could bring timber to Pekanbaru without the necessary permits.

The survey revealed that there were three main types of timber supply chains in Pekanbaru, as depicted in Table 7.7. There were three stages of supply, namely, logging in forests, distributing logs to mills, and processing logs into lumber or timber in molding mills and, sometimes, in timber kiosks. Although kiosks had no machines to process the timber, kiosks managed timber production, with workers processing logs into lumber or timber in places other than the kiosks. In the first category, workers at the molding mills and timber kiosks managed all stages of the timber supply. This type of operation often managed large concessions and, for this reason, did not require many networks to secure adequate timber supply. The molding mills and timber kiosks that processed and retailed timber for wooden pallets fell into this category.

The second category involved timber distributors working together with molding mills and timber kiosks. The timber distributors often contacted loggers to keep abreast of availability and logging conditions. After preparing logs or lumber for transport, they called the molding mills and timber kiosks. The molding mills and timber kiosks paid half of the money needed for the distributors to buy the logs and covered the costs of transportation and labor. Upon receipt of the logs, they paid the remaining balance. In the case of timber kiosks, the bulk of the timber processing

Table 7.7 Timber supply chain types of the surveyed molding mills and timber kiosks (Source of data: Aurthor's field survey)