The study area is Deramakot Forest Re-serve (DFR) situated in Sabah (North Bor-neo, Malaysia, 117◦30’ E, 5◦25’ N, 130-300 m asl.). Deramakot has a perhumid climate typical for the inner tropics. Mean annual temperature is 27◦ with little sea-sonal variations. Average annual precip-itation is about 3500 mm, with no pro-nounced dry season. The geology of De-ramakot is characterised by tertiary sedi-ments, mostly sandstones. The soils are nutrient-poor and prone to erosion once left devoid of tree cover. The prevailing forest type is lowland dipterocarp forest (Schlen-sog 1997). The forest remained essentially undisturbed until this century. Commer-cial logging started in 1956. The intensity of logging and of logging damages varies widely (Kilou et al. 1993). In 1991 the Sabah Forestry Department carried out a terrestrial inventory. All trees with a diame-ter≥10 cm in 0.25 ha sample plots regularly distributed in a 1×1 km grid over the whole reserve of 55,000 ha were recorded. Average basal area was 20.9 m2 ha−1 (SD=9.2 m2 ha−1; range: 1.3-57.8 m2 ha−1), indicating logged-over forest compositions (Kilou et al.
1993; K¨ohler 1998).
Within the Deramakot Forest Reserve, eight research plots with different degrees of disturbance were analysed for tree species composition and forest structure (Schlen-sog 1997). All trees with a diameter at breast height d≥30 cm were measured in plots of 90×90 m, small trees (d≥10 cm), saplings (height≥1.5 m and d<10 cm) and seedlings (height<1.5 m) in nested plots of 30×30 m, 30×5 m, and 59 plots of 1×1 m, respectively. Out of the eight plots, three were primary forest stands possibly never logged, four were logged-over stands (two
with few pioneers, two dominated by pio-neers) and the last one was a recently (one year prior) logged stand using methods of reduced-impact logging.
For the purpose of our simulations, we choose the recently logged stand (plot 4 in the work of Schlensog, labelled L1 in Huth et al. 1998 and here) as an example of for-est structure after logging, which also cor-responds to most of the forests in DFR, and one of the primary forest stands (plot 1, re-ferred to as P1 here) for reference.
Species grouping
Shrub and tree species (total number:
468 species) were assigned to 13 different plant functional types (PFT) based on their successional status and maximum height at maturity (Table 7.1). The suc-cessional status (early, mid, or late) was determined by their growth rates under various light regimes, as well as through a survey of wood densities, which are good indicators of growth rates for most species. Species list including grouping is available online (http://www.usf.uni-kassel.de/usf/archiv/dokumente.en.htm, Appendix of thesis, Table B.2). A detailed description and validation of the grouping concept and its application to Sabah was published elsewhere (K¨ohler et al. 2000b).
Similar grouping concepts are found in Swaine and Whitmore (1988), Manokaran
& Swaine (1994), Thomas & Bazzaz (1999), and Kammesheidt (2000). In addition, a subgrouping into commercial and non-commercial species is performed for all mid and late successional species. Since detailed information of the commercial status was not available at the individual tree level, 80 % of mid and late successional species are considered as commercial timber (Sabah-Forestry-Department 1994). A suf-ficient number of PFTs is essential for the accuracy of the output in the simulation of highly diverse rain forests. However, in the analysis and for the sake of simplicity, we
Table 7.1: Autecological characteristics of 13 plant functional types (PFTs) of Sabah’s lowland tree species.H: Height at maturity.SS: successional status.HG: height group.COM: fraction of commercial species in PFT.No: Number of species per PFT (total 468 spp.). P1, L1:
Abundance of trees with diameter>10 cm in plot P1 and L1, respectively.Sum of abundances might not match 100 % due to rounding errors.(Modified from K¨ohler et al.2000b.)
Plant functional type H [m] PFT SS HG COM [%] No P1 [%] L1 [%]
Shrub mid succ. spp. 0-5 1 2 1 0 15 0.0 0.0
Understorey early succ. spp. 5-15 2 1 2 0 5 0.0 4.6
Understorey mid succ. spp. 5-15 3 2 2 80 28 6.9 1.3
Understorey late succ. spp. 5-15 4 3 2 80 65 6.4 0.8
Lower canopy early succ. spp. 15-25 5 1 3 0 14 0.7 65.8
Lower canopy mid succ. spp. 15-25 6 2 3 80 92 18.8 2.1
Lower canopy late succ. spp. 15-25 7 3 3 80 13 0.2 0.8
Upper canopy early succ. spp. 25-36 8 1 4 0 10 0.0 4.6
Upper canopy mid succ. spp. 25-36 9 2 4 80 89 6.6 4.6
Upper canopy late succ. spp. 25-36 10 3 4 80 18 3.6 2.5
Emergent early succ.spp. >36 11 1 5 0 3 0.0 0.4
Emergent mid succ.spp. >36 12 2 5 80 93 37.0 11.7
Emergent late succ.spp. >36 13 3 5 80 24 19.5 0.0
distinguish results only between the three different successional status (early, mid, and late).
Description of the model
Formind2.0 is an individual-oriented process-based forest growth model (K¨ohler
& Huth 1998a; K¨ohler et al. 2001) to simulate spatial and temporal development of uneven-aged mixed forest stands. A complete description including all the relevant functional relationships of the model version Formind2.0 was published elsewhere (Kammesheidt et al. 2000). We concentrate in the following after a short general description on the recruitment submodel. Basic functions are shown in the Appendix (of article).
The model describes forest dynamics as
a mosaic of interacting forest patches of 20 m2×20 m2 in size. Within these patches trees are not spatially explicit distributed, and thus all compete for light and space following the gap model approach (Botkin 1993; Shugart 1998). Allometric relation-ships connect above-ground biomass, stem diameter, tree height, stem volume and crown dimensions. Using these allometric relationships, the distribution of individ-ual tree crowns and their leaf area in the canopy is calculated in horizontal canopy layers with a depth of 0.5 m.
The growth of an individual tree is based on a carbon balance. Calculations include photoproduction of the trees and assimilate losses due to respiration, litter-fall and fine root decay. Within a patch, vertical light attenuation in the canopy is calculated from light intensity above the canopy with
re-spect to the absorption of tree crowns. The dependence of specific photosynthetic pro-ductivity on irradiance is modelled using a Michaelis-Menten-type light response curve.
Photoproduction ˜P is calculated from the tree’s leaf area (Monsi & Saeki 1953). We assume an increasing limitation effect of wa-ter transport deficiencies with growing tree height (Ryan et al. 1997). Thus, actual pro-ductivity is calculated by applying a size-dependent limitation factor q(d) (according to the aging factor of Landsberg & Waring 1997). Assimilate losses are estimated in re-lation to tree biomass B (Kira 1978; Yoda 1983). We distinguish between a biomass-dependent maintenance respirationRm and growth respirationRG (Ditzer et al. 2000).
Our main time-dependent growth equation for one tree i is
dBi
dt = ˜Pi·q(di)(1−RG)−Rm(Bi) (7.1) Tree growth is calculated in annual time steps.
Competition is modelled in terms of com-petition for light and space, the latter re-sulting in self-thinning.
For small trees (diameter < 10 cm a diameter-dependent mortality is added to a basic mortality rate. Trees resulting in crown closure are eliminated to avoid crowding (self-thinning). Mortality is mod-elled as stochastic event. Senescent trees (≥ 10 cm d.b.h.) die and collapse with a certain probability, knocking down smaller trees in neighbouring patches thereby creating gaps of a size that depends upon their crown size.
Two different recruitment mechanisms were incorporated in the model. The sim-plest approach consists in assuming that an intact forest is supporting a constant seed input rate. The second takes into account the dispersal of seeds produced from local mother trees, i.e. trees exceeding a certain diameterDR. As recruitment strategies are highly variable in rain forests, with interspe-cific differences in fruiting period, number of seeds, seed sizes (Leishman et al. 1995),
dispersal strategies, agents, and distances, seed survival, germination probabilities and maturing size of seed disperser (Garwood 1983; Whitmore 1983; Denslow 1987) some fundamental assumptions on the most im-portant trends have to be made.
Flowering, fruiting and seed produc-tion vary in duraproduc-tion and frequency across species, some species fruiting after several years of unfecundity (Garwood 1983; Cur-ran & Leighton 2000). Other species flower and fruit continually throughout the year in Malaysian rain forests (Putz 1979). Sea-sonal differences in seed production are not taken into consideration. The rate of seed production varies widely among species (Whitmore 1998). Various studies have analysed different dispersal strategies and lengths (review in Clark et al. 1999b). Dif-ferent dispersal agents (e.g. wind, birds, mammals) are not directly distinguished in our model, but the resulting average disper-sal distance XR depends upon the species and should match with the parameter set.
From the dispersal kernels discussed by Clark et al. (1999) we use the Gaussian dis-tribution (as used by Chave 1999b). As-suming rotation symmetry, the probability density f of seeds to be dispersed at the distance r from the mother tree is
f(r) = 2r with cd, the crown diameter (see Fig. 7.1). Thus, 99% of the seeds are dispersed in a distance less than 2.14×(XR+cd/2). The actual dispersal distance r is randomly drawn from this probability distribution, and the direction is drawn uniformly. The resulting seed shadow is the product of the rate of seed production and the dispersal kernel (Clark et al. 1999b).
For both recruitment mechanisms, in-coming seeds update a seed pool, tak-ing into account the dormancy variability across functional groups (cf. Garwood 1983,
0 100 200 300
Figure 7.1: Seeds dispersal kernels for a Gaussian distribution with different average dispersal distances XR.Crown diameter was fixed at cd=20 m.
1989). These seeds correspond to the repro-ductive success and are those which can po-tentially be established at the minimum di-ameter of 1 cm (Ribbens et al. 1994; Chave 1999b). Seed loss due to predators is implic-itly included in relative low seed production rates. The actual seed germination depends upon understorey light intensities.