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Application of Forest Gap Model for Sal () Forest Succession การประยกตุ ใช์ แบบจ้ าลองชํ ่องวางในป่ ่าสาหรํ ับการทดแทนของป่าซาล ์

Dipak Jnawali, Raywadee Roachanakanan and Kulvadee Kansuntisukmongkol Faculty of Environment and Resource Studies, Mahidol University, Nakhonpathom 73170

Abstract The Shorea roubsta (sal) forest dominated in lowland of has ecologically and economically significant values. Chitawan National Park (CNP), a protected area in central lowland of Nepal was selected for the study and species composition, basal area and stem density were recorded and measured from 20 sampling plots. Together with secondary and primary data from consultation with experts, all data were used to reparameterize the KIAMBRAM model. The KIAMBRAM model is an individual-based forest gap model in the JABOWA-FORET model family and is developed for subtropical rainforest in Australia. The application of the KIAMBRAM model for prediction of the natural sal forest stand dynamics of subtropical region of Nepal is the goal of the study. Four major components (subroutines); GROW, BIRTH, KILL and CHABLI of the KIAMBRAM model were selected. The model was first test through the qualitative comparison of species composition of each successional stage, i.e., early successional stage, mid-successional stage, late successional stage and mature stage. Species composition from the simulated model results at mature stage was compared with field data, whereas for other stages the simulated model results were compared with those of available literature. The results showed that the best match between the simulated model results and data of CNP forest was for mature stage and also for early successional stage where the results were satisfactorily matched whereas for mid-successional and late-successional stages, the results were fairly matched. It can be concluded that the KIAMBRAM model has a potential to be used as a tool to gain knowledge on the succession of sal forest dynamics in Nepal.

Key words : the KIAMBRAM model, individual-based gap model, Shorea roubsta, forest succession

ป่าซาล ์ (Sal) (Shorea robusta ) เป็นป่าที่ตั้งอยในบรู่ ิเวณพ้ืนที่ราบตาของประเทศเนปาลํ่ ซึ่งมีความสาคํ ญในทางั นิเวศวิทยาและเศรษฐกิจ และเป็นป่ าในเขตสงวนของอุทยานแห่งชาติชิตวนซั ึ่งเป็นพ้ืนที่ราบตาตอนกลางของํ่ ประเทศเนปาล ได้ถูกคดเลั ือกข้ึนมาใชเป้ ็นกรณีศึกษา คลอบคลุมพ้ืนที่ตัวอยางท่ ้งหมดั 20 แปลง ซึ่งเป็นป่าที่มี ช่วงการเจริญเติบโตสมบูรณ์เต็มที่แล้ว เพื่อใชเป้ ็นเครื่องมือในการศึกษาการทดแทนป่าโดยได้นําแบบจาลองํ ช่องว่างในป่ามาประยุกตใช์ ในป้ ่าซาล์ซึ่งสามารถใชเป้ ็นตนแบบของป้ ่ าในเขตร้อนได ้ ซึ่งการศึกษาดงกลั ่าวมี การศึกษาใน 4 ปัจจยหลั กทั ี่สําคัญ คือ การเจริญเติบโต การเกิด การตาย และ การเกิดช่องวางของแบบจ่ าลองชํ ื่อ KIAMBRAM โดยใช้ตัวแปรของชนิดที่ไดจากการทบทวนวรรณกรรม้ ในแบบจาลองเพํ ื่อศึกษาพลวตของปั ่ า ซาลในช์ ่วงต่างๆ ของการทดแทน ไดแก้ ่ ช่วงต้น ช่วงกลาง ช่วงปลาย และช่วงสมบูรณ์เตมท็ ี่ จากการศึกษาวิจัย สามารถสรุปผลได้วาการน่ าแบบจํ าลองชํ ่องวางในป่ ่าสามารถนามาประยํ ุกตใช์ ในการศ้ ึกษาการทดแทนในป่า

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คําสําคัญ : แบบจาลองํ KIAMBRAM/ แบบจาลองชํ ่องวางแบบป่ ัจเจก/ Shorea robusta/ การทดแทนของป่า

1. Introduction

Sal forest is prevalent in tropical and subtropical regions of South Asia and distributed on the plain and lower foothills of Himalayas (Gautam and Devoe, 2006), Siwalik hills and river valleys (Jackson, 1994). This forest covers about 10 million ha in India (Tiwari, 1995), 1 million ha in Nepal (HMGN, 1988), 0.11 million ha in Bangladesh (Alam, 1996) and some parts in Bhutan (Gautam and Devoe, 2006). In Nepal, sal forest is the major component (more than 70%) of Terai forest (lowland tropical and subtropical forests). Sal forest is ecologically and economically important (commercial and subsistence purposes) for tropical and subtropical area of Nepal (Kandel and Shrestha, 2001; Webb and Sah, 2003). The Terai forest in Nepal is broadly categorized into two types; protected area based management system and open access government managed forest outside the protected area. Total five protected areas including two National Parks and three Wildlife Reserves are executing under the protected area based management system in the Terai forest of Nepal and they cover almost half of the Terai forest area (Timilsina, 2005). Forests outside the protected area in the Terai forest of Nepal are partly managed under community based management system such as community forest and the rest are managed by government but are open access for local people.

Despite their widespread occurrence and importance of sal forest in Nepal, little information exists on ecological aspects. Past studies on sal forest inside the protected area (natural forest) focused mostly on floristic structure and composition (Dinerstein, 1979; Shrestha and Jha, 1997; Timilsina et al, 2007). No information is available on dynamics of the sal forest. Wesche (1997) described different types of the sal forest in natural forest of Central Nepal. Nagendra (2002) also studied the forest condition outside the protected area in Chitawan (Central Nepal). Rautiainen (1999) and Rautiainen et al (2000) focused on growth and yield models of sal species (not sal forest) however they did not incorporate the stand and sub-stand level dynamics. There is completely lack of knowledge on disturbance, growth, birth and kill factors influencing on successional change of forest composition in natural sal forest. Information on forest dynamics is very important at the managerial level to make the decision on biodiversity conservation and management because sal forest in Nepal is the habitat for large and endangered mammals such as rhinoceros, Asian tiger and Asian elephant (Shrestha, 2004).

Establishment of the sal seedling at the initial phase requires special soil condition and such soils could be new alluvial deposits, sand dunes and land slips (Troup, 1921). The early stages of the

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succession are generally easily recognized as they are generally uniform, but in the later stages, great divergence is noticeable due to moisture and soil conditions (Khanna, 1993) and it becomes difficult to identify and correlate with environmental factors. Succession of sal forest on Gangetic riverine alluvium of Uttar Pradesh of India was observed by Khanna (1993) and it was described that at the very beginning stage special types of grasses took place as the pioneer species in the early successional process. Most common pioneer grasses were Saccharum spontaneum and Tamarix dioica. After the time, two major tree species i.e., Acacia catechu and Dalbergia sissoo replaced the grassland. Then, in the course of the time A. catechu and D. sissoo were slowly replaced by three species i.e., Holoptelia sp., Adina cordifolia and Albizzia procera. In the mid-successional stage, some new species such as Lagerstroemia parviflora, Bombax ceiba and Terminalia belerica existed in the community whereas A. cordifolia, L. parviflora and T. belerica played dominant roles in the stand. In the late successional stage, Shorea robusta established in the community and other species which were similar with those of the mid successional stage. In this stage Terminalia alata also took place if the soil contained moisture. At the climax stage, S. robusta became the most dominant species. Other dominant species at the climax stage could be L. parviflora, Terminalia spp. and A. cordifolia. At this stage, Syzygium cumini also appeared at the lower canopy level in the site with moisture content.

Formation of gap and gap dynamics are the important driving force that maintains the tropical diversity (Young and Hubbell, 1991). Gap models are developed for better understanding of the ecological mechanism and patterns of structure and functional dynamics in natural forest ecosystems over a long period of time (Liu and Ashton, 1995; Kolstrom, 1998). Individual-based forest gap models are a potential tool for addressing succession dynamics because of their focus on individual tree growth and species-specific life history parameters (Botkin et al, 1972; Botkin, 1993; Shugart et al, 1980; Shugart, 1984). Most of the gap models in tropical and subtropical forests are developed from the JABOWA-FORET model (Roachanakanan, 2002). Among the ancestors of the JABOWA- FORET model, there are only few models that were developed for tropical forests. The KIAMBRAM model was developed by Shugart et al (1980) for the complex subtropical rainforest in Australia and was further reparameterized to another two subtropical and tropical rainforests in Australia by Roachanakanan (2002).

For the present study, the KIAMBRAM model was reparameterized without any change on the model structure to the lowland sal forest in Nepal. Four major components (subroutines); GROW, BIRTH, KILL and CHABLIS of the KIAMBRAM model were selected. The objective of the present study is to test the model performance through the qualitative comparison of species composition of each successional stage, i.e., early successional stage, mid-successional stage, late-succession stage and mature stage.

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2. Materials and methods

2.1 The Study area Chiawan National Park (CNP), a natural undisturbed forest of subtropical region of Nepal was selected for the study. CNP is also in UNESCO world heritage list since 1984 for its internationally important on flora and fauna diversities (Straede et al, 2002). The CNP covers a pristine area of 932 km2 as a core area and additional buffer zone area of 750 km2. The CNP is situated between longitude 84°, 20'E. and latitude 27°, 30'N. The climate is subtropical with high humidity throughout the year. Mean annual rainfall is 1900 mm, with 80% as short and heavy rainfall in the summer monsoon season from June through September. The minimum daily mean temperature is 16°C in January and 31°C in May through July (Rampur Weather Station, pers. comm., 2007). Soil of the lower altitude of the CPN is largely alluvial deposits left by shifting river courses ranging from sandy and coarse loam on new terraces to silty clay loam on older terraces (HMGN, 1968). The most dominant forest type of the CNP is climax sal forest (Mishra, 1982). About 70% of the park vegetation is predominantly Shorea robusta (Sal) forest. The other vegetation types include riverine forest (7%), hill forest (3%) and grassland (20%) (Shresha and Dangol, 2006). Riverine forest can further be categorized into five different types (Mishra, 1982); (i) Acacia–Dalbergia forest dominated by Acacia catechu and Dalbergia sissoo, (ii) Bombax–Trewia forest dominated by Bombax ceiba and Trewia nudiflora, (iii) Evergreen Albizia forest dominated by Albizia lucida and Mangifera indica, (iv) Eugenia woodland dominated by Syzygium cumini and Syzygium operculata and (v) Bombax– forest dominated by Bombax ceiba and Litsea monopetala.

2.2 Measurements A mature sal forest block near the Kasara office (the headquarters of the CNP) was selected for the sample measurement. In total 20 sampling plots were established and measured along two transect lines. First transect line was in east–west direction and about 2 km east from the CNP headquarters. The second transect line was in north-west direction. The 10 sampling plots were established at the equal interval of 100 m on each line. The sampling plot was 500 m2 and in circular shape. Diameter at breast height (DBH) and height of the all trees having >1.27 cm DBH were measured. Diameter tape and Santo clinometer were used for measuring DBH and height, respectively. Also the calibrated thin bamboo stick was used for height measurement of the smaller trees. Types of disturbances in the sampling plot were observed and recorded if any. Physical conditions of grassland and soil were also observed. Discussion with the park authority on fire frequency and its impact, impact of grass collection was carried out and recorded. Forest inventory work was carried out from August to September, 2007.

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2.3 Data preparation Three types of data; species list, species specific parameters (SPSPs) and site specific parameters (SISPs) were prepared to provide the input for the KIAMBRAM model; a) the tree species list of sal forest of lowland of Nepal was prepared from the literature at the broader level in the first version, verified during the empirical study in the field and finalized after the completion of the field work. Altogether 55 tree species were recorded (see Appendix-I); b) SPSPs for all 55 species were from various methodologies including secondary data such as literature, published and unpublished materials/ reports and web based information, primary data such as consultation with experts, participation of local people, personal experience of researcher and mathematical manipulation. All the SPSPs are presented in Appendices-II and III; c) most of SISPs were used without any change however for some SISPs, i.e., light extinction coefficient (k), SOILQ and tree fall probability, the new values were assigned as shown in Appendix-IV.

2.4 Modeling and test GROW, BIRTH, KILL, CHABLI, STRGLE and LUMBER are six subroutines in the KIAMRAM model. Two subroutines of the model, STRGLE and LUMBER, were neither calibrated nor used in the simulation process in this study. The structure and process of the KIAMBRAM model are summarized in Appendix-V. SPSPs and SISPs mentioned in Section 2.3 were used as the model inputs for the KIAMBRAM model. Each model simulation started from a bare plot and each model result was obtained from the average of 100 runs over the period of 1,000 years. The succesional stages were defined into four stages; early, mid, late, and mature stages. Species composition from the simulated model results at mature stage was compared with field data whereas for other stages simulated model results were compared with available literature of riverine sal forest decribed by Khanna (1993).

The version of the KIAMBRAM model, developed by Prof. Dr. Ian Noble and Mr. Ian Davies in 1994, was used in the present study with the permission of the developers.

3. Result and discussion

3.1 Comparision of species composition between model results and sal forest The model results of species compositions simulated by the KIAMBRAM model under the conditions used and described in Sections 2.3 and 2.4 were compared with those both from literature (Khanna, 1993) and observation (field survey).

3.1.1 Early successional stage The KIAMBRAM model predicted 30 species at this stage (Table 1). The model result showed that Acacia catechu and Dalbergia sissoo were most dominant species. In the later years of this stage,

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number of D. sissoo was declining. Other lesser dominant species at this stage were Albizzia leebak, Dalbergia latifolia, Largerstroemia parviflora, Terminalia belerica and Shorea robusta. Adina cordifolia was nominal abundant with very small sized trees. Anogeissus latifolius and Anthocephalus chinensis were found only one individual each. Similarly, other species such as; Bischofia javanica, Cassine glauca, Dillenia pentagyna, Garuga piñnata, Randia dumetorum and Trewia nudiflora were very nominal abundant fews individuals) with small sized trees.

According to Khanna (1993), there are only two dominant species of A. catechu and D. sissoo in this stage. He did not mention any other species. But, model results showed that three species; D. sissoo, A. catechu, and L. parviflora first pioneer species and leading in the stand. However, A. catechu and D. sissoo older aged (earlier established) than L. parviflora. Thus, the KIAMBRAM model simulated the species composition for two major species, i.e., A. catechu and D. sissoo. Due to lack of other species composition references or empirical data for this stage, it is difficult to make discussion whether simulated species are matching with real condition or not. Khanna (1993) mentioned that L. parviflora is late successional or early climax species in the riverine sal forest succession, but the model simulated the species as a major pioneer species which is not matching. According to the model results, A. leebak, D. latifolia, T. belerica and S. robusta also observed pioneer species but T. belerica and S. robusta species are either mid- or late successional species for riverine succession forest (Khanna, 1993). Therefore, the model result in context of T. belerica and S. robusta species was not matched. But simulated result for A. leebak, D. latifolia species was not matched with riverain forest succession for this stage but these two species were matched with Webb and Sah (2003)’s data set for 20 years secondary successional forest in Central Nepal. Model simulated total number of specie also observed higher in number but without empirical data it is difficult to make concrete decision. It can be conclude that the KIAMBRAM model is able to closely simulate the survive composition of the early succesional pioneer species with exceptional of some extra species.

3.1.2 Mid-successional stage The KIAMBRAM model simulated 33 species at this stage (Appendix-VI). The model results showed four most dominant species of A. catechu, D. sissoo, Cedrela toona, and T. belerica where first two are slightly declining in term of abundant compare to previous stage. Simulated result also shows Cleistocalyx operculatus has higher abundance in this stage. Similarly, Cassia fistula and D. latifolia also have older and considerable abundant. Mallotus philippensis, Pterocarpus marsupium, Syzygium cumini and C. toona were observed in the simulations in this stage first time that they were not in the earlier stage. Condition of another focal species S. robusta has fewer abundant but individuals were more than 45 years old. Rest of simulated species has less number, younger than 20 year (recruit after previous stage) and smaller in size.

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Table 1 List of species of different successional stages from the simulated model outputs

Pioneer stage Late-successional stage Mature stage 1 Dalbergia sissoo Shorea robusta Shorea robusta 2 Shorea robusta Dalbergia latifolia Largerstroemia parviflora 3 Dalbergia latifolia Cedrela toona Adina cordifolia 4 Litsea monopetala Cleistocalyx operculatus Cedrela toona 5 Acacia catechu Terminalia alata Cleistocalyx operculatus 6 Terminalia bellirica Bombax ceiba Cassia fistula 7 Bauhinia purpurea Terminalia chebula Litsea monopetala 8 Albizia lebbek Adina cordifolia Syzygium cumini 9 Cleistocalyx operculatus Cassia fistula Dillenia pentagyna 10 Bombax ceiba Litsea monopetala Terminalia bellirica 11 Largerstroemia parviflora Diospyros malabarica Butea monosperma 12 Anthocephalus chinensis Bauhinia variegata Grewia optiva 13 Bauhinia variegata Cassine glauca Bauhinia variegata 14 Phyllanthus emblica Largerstroemia parviflora Aegle marmelos 15 Cassia fistula Carpesium nepalense Myrsine semiserrata 16 Terminalia chebula Butea monosperma Diospyros malabarica 17 Terminalia alata Dillenia pentagyna Cassine glauca 18 Aegle marmelos Garuga pinnata Buchanania latifolia 19 Diospyros malabarica Pterocarpus marsupium 20 Ficus beghalensis Terminalia bellirica 21 Adina cordifolia Phyllanthus emblica 22 Holarrhena pubescens Duabanga grandiflora 23 Butea monosperma 24 Dillenia pentagyna 25 Bischofia javanica 26 Trewia nudiflora 27 Cassine glauca 28 Garuga pinnata 29 Anogeissus latifolius 30 Randia dumetorum

Comparing above simulated result with available references following conclusion were draw for this stage. (1) Higher abundance and dominant role of A. catechu and D. sissoo were matched with Khanna (1993). (2) Simulated new species M. philippensis, P. marsupium, S.cumini and C. toona were not matched because these species were observed at late succession stage and climax stage species in United Province of India (Troup, 1921), and Uttar Pradesh (Khanna,1993). (3) Having older aged sal individuals in the simulated result indicates that they were continuing from early stages, but in contrast sal species is late successional/climax species (Khanna, 1993), therefore it is also not

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matched. (4) It is difficult to comment to the rest other simulated species composition for this stage due to lack of sufficient references.

3.1.3 Late successional stage Total 23 species were observed in model results of this stage. All the species of mid-succesional stage were also found in this stage expect Duabanga grandiflora which was a new species at this stage. A. catechu and D. sissoo (major pioneer species) were not simulated by model. Similarly, Bauhinia purpurea and Bauhinia variegate were not found in simulated result at this phase but that were presented in early phases. It indicates that shorter live pioneer species have been replaced by other long-lived late succesional species in the community at this stage. In simulation, S. robusta has dominated upper canopy followed by D. latifolia, T. alata, A. cordifolia and C. toona. Similarly, at the lower canopy level C. fistula, Litsea monopetala and C. operculatus were more abundant in the simulation. Simulation shows very low abundances of Bischofia javanica, Bombax ceiba, Butea monosperma, D. pentagyna, Diospyros malabarica, Duabanga grandiflora, Garuga pinnata, Phyllanthus emblica, P. marsupium, Terminalia chebula at this stage. One new species Duabanga grandiflora has introduced at this stage. M. philippensis and S. cumini were observed in mid- successional stage but were absent at this stage.

After comparing above simulated results with species composition of riverine sal forest succession at late-successional stage from Table 2.3 (Khanna, 1993), following conclusions were made. (1) Absent of A. catechu and D. sissoo species in the simulation is matched with similar stage of riverain sal forest succession (Khanna, 1993). (2) Dominancy of S.robusta in simulation at this stage is not matched, because of major three species i.e. Adia, lagerstroemia and Terminalias should show dominancy than S. robusta at this stage (Khanna, 1993). (3) Due to lack of sufficient empirical data about the species composition for this stage, it is difficult to make comment about simulated species composition whether they should present at this stage or not.

3.1.4 Mature stage Re-parameterized model simulated result at this stage shows only 18 species. In the simulation, S. robusta found extremely dominated species in term of higher height, bigger diameter and fairly abundant. Most of the individuals of S. robusta found over 200 Year old in the simulated result. Due to these characteristics of the S. robusta, simulated forest is even aged but not close to monoculture. At the co-dominant level; L.parviflora, A. cordifolia, C. operculatus and C. toona were presences in the simulation. Similarly, simulation result shows; L. monopetala, C. fistula, D. pentagyna and T. bellirica in the lower canopy layer. Rest other species found nominal frequency and smaller sized in the simulation result. In this stage, model simulates three new species i.e. Diospyros malabarica, Myrsine semiserrata and S. cumini that were not found in earlier stage. In contrast, some important

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species includes; T. alata, T. chebula and P. emblica were not found in simulated result, where they were presented in the earlier stage.

Species composition between above simulated result of mature sal forest composition and observed mature forest of the core area of CNP were compared. Fortunately, there were availability of few other references of species composition of the mature stage forest and they were also considered in this comparison and following points were summarized. (1) Dominancy of S. robusta in the simulation is exactly matched with observed forest condition, where S. robusta also found dominate species (section 4.1). (2) Simulated result shows that forest is near to even aged is matching with characteristics of mature S. robusta forest described by Troup (1921), Jackson (1993), Tiwari (1995), Nagendra (2002), Pandey and Shukla (2003), Rautiainen (1999), Rautiainen et al (2000), Gautam and Devoe (2006). (3) Presences of Adia, Lagerstroemia, Terminalias and S. cumini at the lower canopy level in the model simulated result, is exactly mating with major species composition of mature S. robusta forest explained by Khanna (1993) and Jackson (1994). (4) Simulated result shows C. toona is also dominant species but this species was not observed in mature forest filed, which is not matched. (5) In the observation; L. monopetala, D. pentagyna, C. operculatus, H. pubescens, M. philippensis, Aporusa octandra, B. purpurea, T. nudiflora were found more abundant species in lower canopy but reparameterized model is not able to simulate H. pubescens, M. philippensis, A. octandra, B. purpurea, T. nudiflora at this stage, but these species were simulated at mid-succession and early climax stages, therefore partially satisfied in this context. Evaluating above five conclusive points, species composition of reparameterized model’s simulated result and observed forest were moderately matched.

3.2 Observed mature forest condition Altogether 42 tree species (with DBH>1.27 cm) were observed in the study site. S. robusta was most dominant species followed by L. monopetala, D. pentagyna, Cleistocalyx operculatus, Holarrhena pubescens, L. parviflora, M. philippensis, A. octandra, B. purpurea, T. nudiflora and Actinodaphne angusifolia. Rest other species were nominal abundant. The higher canopy layer was purely dominated by S. robusta. Occasionally T. bellirica were present in the higher canopy layer. The sub- canopy was dominated by H. pubescens, D. Pentagyna, C. operculatus, L. monopetala and L. parviflora. Other abundant species at this stage were; M. philippensis, A. octandra, B. purpurea, T. nudiflora and A.angusifoli.

S. robusta found higher abundant at higher sized and higher aged. They found limited number at middle aged. Some other aged species that we found were; C. operculatus, T. bellirica and H. pubescens but their abundant was very low. We also did not found much species in the middle aged and middle size class. Tree species at the understory level found very poor. The understory was mostly covered by grasses and some species of shrubs, which were not accounted in the sampling

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process. Grass dominated plots were relatively lower elevated and some how connected with other grass land area. Due to larger sized and over aged fewer number of trees and poor stock at middle and lower canopy level we conclude our observed forest was climax forest. The soil condition was well drainage deep, moist, and fertile. Soil surface is found not eroded. Therefore we also notice that soil condition was edaphically climax.

3.3 Limitation of the study There were many limitations in this study, such as: (a) one study site was taken as a observation of mature forest which might be not sufficient representation of the mature sal forest of lowland of Nepal, (b) due to unavailability of the past research, most of species specific parameters were gathered with verbal communication, estimation and manipulation, (c) determination of site specific parameters was rather difficulties, therefore most of them used as same value assumed in original model, (d) major disturbance factor in sal forest is forest fire, which is not included in the model structure, and (e) sal trees grow only in specific soil condition, to reach the climax level soil also should reach climax level called edaphically climax. This part is not included in the model structure. Beyond these many limitations, our simulation shows fair to good agreement with all the stages in the context of species composition. It was suggested that further investigation is necessary for in-depth determination of species specific parameters and site specific parameters, modification of site parameters such as; edaphic factor, impact of forest fire, modification on recruitment component and other relevant to site specific information.

4. Conclusion

The goal of the model was to set the sal forest prediction model to study the dynamics of sal forest succession. The study was significant because sal forest has higher ecological and economical importance in Nepal which directly contributes the country’s biodiversity conservation and subsistence livelihood. The special type of Individual-based forest gap model from similar climatic zone was choose and re-parameterized. New species list, their specific parameters and some of site related parameters were changed in parameterization through verities of sources and methods but structure of model was not changed. The model is somehow able to satisfactory reproduces species composition dynamics at various stages. The simulated climax forest was more compatible with our empirical data than other stages. Predicted numbers of species were fewer than field observation at this stage. It is concluded that application of individual-based gap model is potential to study the succession of sal forest dynamics, if it is properly constructed through considering all influencing factors. Indeed, further study is necessary to answering the conflicting results and made precise prediction model for wider coverage of sal forest dynamics, which would play the role to contribute new paradigm on ecological theory.

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5. Acknowledgement

The authors are gratefully acknowledged to Thailand International Development Cooperation Agency (TICA) for supporting sustainable natural resource conservation work in ecological aspect through financial support for this research. The authors are thankful to Department of National Parks and Wildlife Conservation, Kathmandu office and Chitawan National Park office and the staff for giving permission and other help during the field survey.

6. References Alam, M.K. 1996. Diversity in the woody flora of sal (Shorea robusta) forest of Bangladesh. Bangladesh. Journal of Forest Science. 24: 41-51. Botkin, D.B., Janak, J.F., and Wallis, J.R. 1972. Some ecological consequences of a computer model of forest growth. Journal of Ecology. 60: 849-872. Botkin, D.B. 1993. Forest Dynamics, An ecological model. New York, Oxford University Press. Dinerstein, E. 1979. An ecological survey of the Royal Karnali–Bardia Wildlife Reserve, Nepal. Part I. Vegetation, modifying factors, and successional relationships. Biological Conservation, 15: 127–150. Gautam, K.H. and Devoe, N.N. 2006. Ecological and anthropogenic niches of Sal (Shorea robusta Gaertn. f.) forest and prospects for multiple-product forest management. A Review Forestry Advance Access, DOI 10.1093/forestry/cpi063. Forestry. 79: 81-101. HMGN 1968. Soil survey of Chitwan Division. Forest Resources Survey Publication No. 5. His Majesty’s Government of Nepal. HMGN 1988. Master plan for forestry sector of Nepal. Ministry of Forest and Soil Conservation, His Majesty's Government of Nepal. Jackson, J. 1994. Manual of afforestation in Nepal. Ministry of Forest and Soil Conservation, Forest Research Center, Babarmahal, Kathmandu Nepal. Kandel, K.R. and Shrestha, K. 2001. Tropical secondary forest in Nepal and their importance to local people. Journal of Tropical Forest Science. 13 (4): 691-704. Khanna, L.S. 1993. Principle and practice of silviculture. fifth edition, Khanna Badhu, Dehradun, India. Kolstrom, M. 1998. Ecological simulation model for studying diversity of stand structure in boreal forests. Ecological Modelling. 111 (1): 17-36. Liu, J. and Ashton, P.S. 1995. Individual-based simulation models for forest succession and management. Forest Ecology and Management. 73 (1-3): 157-175. Matthewa, R.B. and Pilbeam, C. 2005. Modelling the long-term productivity and soil fertility of maiz/ millet cropping systems in the mid-hills of Nepal. Agriculture, Ecosystem and Environment. 111 (1-4): 119-139.

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Mishra, H.R. 1982. Balancing human needs and conservation in Nepal’s Royal Chitwan National Park. Ambio. 11 (5): 246–252. Nagendra, H. 2002. Tenure and forest conditions: community forestry in the Nepal Terai. Environmental Conservation. 29 (4). Pandey, S.K. and Shukla, R.P. 2003. Plant diversity in managed sal (Shorea robusta Gaertn. f.) forests of Gorakhpur, India: species composition, regeneration and conservation. Biodiversity and Conservation. 12 (11): 2295-2319. Rautiainen, O. 1999. Spatial yield model for Shorea robusta in Nepal. Forest Ecology and Management. 119: 151-162. Rautiainen, O., Pukkala, T. and Miina, J. 2000. Optimizing the management of even-aged Shorea robusta stands in southern Nepal using individual tree growth models. Forest Ecology and Management. 126: 417-426. Roachanakanan, R. 2002. Predicting the dynamics of rainforests in Australia. Research School of Biological Science, Ph.D. Dissertation, Institute of Advance Studies. Canberra, The Australian National University. Shresha, B.K. and Dangol, D.R. 2006. Change in grassland vegetation in the northern part of Royal Chitawan National Park, Nepal. Scientific World. 4: 4. Shrestha, M.K. (2004). Relative ungulate abundance in a fragmented landscape: implications for tiger conservation. Ph.D. Dissertation, University of Minnesota. Shrestha, K.K. and Jha, P.K. 1997. Plant diversity and evaluation of conservation measures in the Royal Bardia National Park (RBNP). A report submitted to World Wildlife Fund Nepal Program, Kathmandu, Nepal. Shugart, H.H. 1984. A theory of forest dynamics. New York, Springer-Verlag. Shugart, H.H., Mortlock, A.T., Hopkins, M.S. and Burgess, I.P. (1980). A computer simulation model of ecological succession in Australian subtropical rainforest, ORNL. Straede, S., Nebel, G. and Risal, A. 2002. Structure and floristic composition of community forests and their compatibility with villagers’ traditional needs for forest products. Biodiversity and Conservation. 11: 487–508. Timilsina, N. 2005. Analysis of forest under different management regime in the western Terai of Nepal and its relation to envirnment and human use. Environmental studies, Collage of Art and Science, Miami, Florida, Florida International University. Timilsina, N., Ross, M.S. and Heinen, J.T. 2007. Community analysis of Sal (Shorea robusta) forest in the western terai of Nepal. Forest Ecology and Management. 241 (1-3): 223-234. Tiwari, D. 1995. A monograph on Sal (Shorea robusta Gaertn. f), Dehraadun, India, International Book Distributors. Troup, R.S. 1921. The silviculture of Indian trees, Volumes I, II and III, Oxford, The Clarendon Press.

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Webb, E.L. and Sah, R.N. 2003. Structure and diversity of natural and managed Sal (Shorea robusta Gaertn. f.) forest in the Terai of Nepal. Forest Ecology and Management. 176(1-3): 337-353. Wesche, K. 1997, A classification of a tropical Shorea robusta forest stand in southern Nepal. Phytocoenologia. 27 (1997): 103–118. Young, T.P. and. Hubbell, S.P. 1991. Crown asymmetry, treefalls, and repeat disturbance of broad- leaved forest gaps. Ecology. 27 (4): 1464-1471.

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Appendix-I: List of tree species for lowland sal forest of Central Nepal used in the KIAMBRAM model

Name Family Local Name Family Local name name 1 Acacia catechu Leguminosae Khair 29 Duabanga Sonneratiaceae Lampate grandiflora 2 Actinodaphne Jhakri Kath 30 Ehretia Cordiaceae Bohari angusifolia wallichiana 3 Adina cordifolia Rubiaceae Haldu 31 Ficus beghalensis Rubiaceae Bayar 4 Aegle marmelos Rutaceae Bel 32 Ficus benjamina - Dumbri 5 Albizia lebbeck Leguminosae Kalo Sirish 33 Garuga pinnata Burseraceae Dabdabe 6 Anogeissus Combretaceae Banjhi 34 Glochidion Euphorbiaceae Mahuwa latifolius velutinum 7 Anthocephalus Rubiaceae Kadam 35 Grewia optiva Tiliaceae Bhimal chinensis 8 Aporusa octandra Euphorbiaceae Hade 36 Gmelina arborea Verbenaceae Khamari 9 Bauhinia purpurea Caesalpiniaceae Tanki 37 Holarrhena Apocynaaceae Indrajau pubescens 10 Bauhinia variegata Caesalpiniaceae Koiralo 38 Hymenodictyon - Bhurkul excelsum 11 Bischofia javanica Staphyleaceae Kainjal 39 Largerstroemia Lythraceae Bod parviflora dhayaro 12 Bombax ceiba Leguminosae Simal 40 Lannea Anacardiaceae Jinjar coromandelica 13 Bridelia retusa Euphorbiaceae Gayo 41 Litsea monopetala Lauraceae Kutmero 14 Buchanania latifolia Anacardiaceae Chirauji 42 Mallotus Euphorbiaceae Sindhure philippensis 15 Butea monosperma Leguminosae Palas 43 Myrsine Myrsinaceae Kalikath semiserrata 16 Careya arborea Lecythidanceae Kumhi 44 Phyllanthus Euphorbiaceae Amala emblica 17 Casearia graveoles Salicaceae Deri 45 Pterocarpus Leguminosae Bijaya marsupium Sal 18 Cassia fistula Leguminosae Rajbriksha 46 Randia dumetorum Rubiaceae Maindal 19 Cassine glauca Caesalpiniaceae Putali Kath 47 Schleichera oleosa Sapindaceae Kusum 20 Carpesium Linnaea Padke 48 Semecarpus Anacardiaceae Bhalayo nepalense anacardium 21 Cedrela toona Leguminosae Tooni 49 Shorea robusta Dipterocarpaceae Sal 22 Cleistocalyx Myrtaceae Camuna 50 Syzygium cumini Myrtaceae Jamun operculatus 23 Cornus oblonga Cornaceae Lati Kath 51 Terminalia alata Combretaceae Asna 24 Dalbergia latifolia Papilionaceae Satisal 52 Terminalia Combretaceae Barro bellirica 25 Dalbergia sissoo Leguminosae Sissoo 53 Terminalia Combretaceae Harro chebula 26 Desmodium Leguminosae Sadhan 54 Trewia nudiflora Euphorbiaceae Gutel oojeinense 27 Dillenia pentagyna Dilleniaceae Tatari 55 Xeromphis Rubiaceae Pedar utiginosa 28 Diospyros Ebenaceae Teju malabarica

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Appendix-II: Species specific parameters for GROW subroutine

Name Dmax Hmax δDmax Agemax G Shade tolerent 1 Acacia catechu 50 2300 1.40 117 322 InTol 2 Actinodaphne angusifolia 45 1600 0.30 264 53 InTol 3 Adina cordifolia 220 4000 0.90 428 72 Inter 4 Aegle marmelos 40 1300 0.40 178 65 Tol 5 Albizia lebbeck 50 1800 1.40 117 252 InTol 6 Anogeissus latifolius 90 2500 1.50 110 208 InTol 7 Anthocephalus chinensis 50 2000 3.50 27 700 InTol 8 Aporusa octandra 40 1800 0.30 232 68 Tol 9 Bauhinia purpurea 30 1700 0.60 88 170 Tol 10 Bauhinia variegata 35 1400 0.60 102 120 Inter 11 Bischofia javanica 35 1800 0.50 120 129 Tol 12 Bombax ceiba 180 4000 2.40 132 267 InTol 13 Bridelia retusa 16 800 0.40 65 100 Tol 14 Buchanania latifolia 100 2000 0.80 226 80 Inter 15 Butea monosperma 70 1500 0.40 320 43 Tol 16 Careya arborea 40 1500 0.20 350 38 Tol 17 Casearia graveoles 30 1200 0.10 522 20 Inter 18 Cassia fistula 60 1500 1.20 92 150 Inter 19 Cassine glauca 80 2200 0.40 352 55 Inter 20 Carpesium nepalense 25 1500 0.30 142 90 Inter 21 Cedrela toona 100 2700 2.00 90 270 Tol 22 Cleistocalyx operculatus 60 2400 0.70 150 140 Tol 23 Cornus oblonga 40 2000 0.40 172 100 Inter 24 Dalbergia latifolia 150 4000 1.40 188 187 Inter 25 Dalbergia sissoo 75 3000 2.50 70 500 InTol 26 Desmodium oojeinense 80 2500 0.40 350 63 Tol 27 Dillenia pentagyna 75 2200 0.50 264 73 Inter 28 Diospyros malabarica 35 1400 0.50 124 100 Inter 29 Duabanga grandiflora 80 3000 0.40 344 75 Inter 30 Ehretia wallichiana 25 1000 0.30 148 60 Tol 31 Ficus beghalensis 100 3000 0.60 290 90 Inter 32 Ficus benjamina 60 2500 0.30 344 63 Tol 33 Garuga pinnata 55 2700 1.40 70 344 InTol 34 Glochidion velutinum 45 1500 0.30 265 50 Inter 35 Grewia optiva 50 2200 0.80 108 176 InTol 36 Gmelina arborea 70 3000 0.50 240 107 InTol 37 Holarrhena pubescens 30 1000 0.60 92 100 Inter 38 Hymenodictyon excelsum 70 1800 0.40 312 51 Inter 39 Largerstroemia parviflora 130 3000 1.80 266 208 InTol 40 Lannea coromandelica 70 2500 0.70 174 125 InTol 41 Litsea monopetala 45 1400 1.50 56 233 Inter 42 Mallotus philippensis 22 1500 0.30 122 102 Tol 43 Myrsine semiserrata 45 2400 0.40 192 107 Inter 44 Phyllanthus emblica 25 1800 0.70 132 252 InTol 45 Pterocarpus marsupium 75 3100 0.60 216 124 Inter 46 Randia dumetorum 15 800 0.07 356 19 Inter 47 Schleichera oleosa 75 2500 0.30 434 50 Tol 48 Semecarpus anacardium 30 1000 0.40 136 67 Tol 49 Shorea robusta 250 5500 2.90 340 319 InTol 50 Syzygium cumini 50 2000 1.20 74 240 Inter 51 Terminalia alata 180 3000 1.00 308 83 Inter 52 Terminalia bellirica 97 3700 1.30 266 248 InTol 53 Terminalia chebula 75 2200 0.80 166 117 Inter 54 Trewia nudiflora 35 1400 0.90 70 180 Inter 55 Xeromphis utiginosa 10 800 0.10 154 40 Tol

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Appendix-III: Species specific parameters for BIRTH subroutine

Seed Seed Bird Wind Grav. Min Raise Name Phenology Longevity Disp. Disp. Disp. Soil d Site 1 Acacia catechu Reg Med N Y N Y N 2 Actinodaphne angusifolia Irreg Short Y N N Y N 3 Adina cordifolia Irreg Med N Y N N N 4 Aegle marmelos Reg Med Y N N N N 5 Albizia lebbeck Reg Reg N Y N Y N 6 Anogeissus latifolius Reg Med N Y N N Y 7 Anthocephalus chinensis Reg Med Y N N Y N 8 Aporusa octandra Irreg Med Y N N Y N 9 Bauhinia purpurea Reg Reg N Y N Y N 10 Bauhinia variegata Reg Short N Y N N N 11 Bischofia javanica Irreg Short Y N N N N 12 Bombax ceiba Irreg Med N Y N Y N 13 Bridelia retusa Irreg Med Y N N Y N 14 Buchanania latifolia Irreg V.Short Y N N N N 15 Butea monosperma Reg Med N Y N N N 16 Careya arborea Irreg Short Y N N N N 17 Casearia graveoles Irreg Med Y N N Y N 18 Cassia fistula Irreg Reg Y N N N N 19 Cassine glauca Irreg Med Y N N N N 20 Carpesium nepalense Irreg Short Y N N N N 21 Cedrela toona Irreg V.Short N Y N N N 22 Cleistocalyx operculatus Irreg Med Y N N N N 23 Cornus oblonga Irreg Short Y N N Y N 24 Dalbergia latifolia Irreg Reg N Y N Y N 25 Dalbergia sissoo Reg Reg N Y N Y N 26 Desmodium oojeinense Irreg V.Short Y N N N N 27 Dillenia pentagyna Irreg Reg Y N N N N 28 Diospyros malabarica Reg Med Y N N N N 29 Duabanga grandiflora Irreg V.Short Y N N N N 30 Ehretia wallichiana Irreg Short N Y N Y N 31 Ficus beghalensis Reg Med Y N N Y N 32 Ficus benjamina Reg V.Short Y N N N N 33 Garuga pinnata Irreg Med Y N N N N 34 Glochidion velutinum Irreg V.Short Y N N Y N 35 Grewia optiva Irreg Reg Y N N N N 36 Gmelina arborea Irreg Reg Y N N Y N 37 Holarrhena pubescens Irreg Short N Y N N N 38 Hymenodictyon excelsum Irreg Med Y N N Y N 39 Largerstroemia parviflora Reg Short N Y N N N 40 Lannea coromandelica Irreg Short N Y N Y N 41 Litsea monopetala Reg Med Y N N N N 42 Mallotus philippensis Reg Short Y N N N N 43 Myrsine semiserrata Irreg Med Y N N N N 44 Phyllanthus emblica Reg Med Y N N N N 45 Pterocarpus marsupium Irreg Short Y N N N N 46 Randia dumetorum Irreg Med N Y N N N 47 Schleichera oleosa Irreg Short Y N N N N 48 Semecarpus anacardium Irreg Med Y N N Y N 49 Shorea robusta Irreg V.Short N Y N N N 50 Syzygium cumini Irreg V.Short Y N N N N 51 Terminalia alata Irreg Med N Y N N N 52 Terminalia bellirica Irreg Reg N Y N N N 53 Terminalia chebula Irreg Short N Y N N N 54 Trewia nudiflora Reg V.Short Y N N N N 55 Xeromphis utiginosa Irreg Short Y N N Y N

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Appendix-IV: Site specific parameters (SISPs) for lowland sal forest

Sub Parameters Value Sources routines Light extinction coefficient (k) 0.37 Matthew and Pilbeam, 2005 1 GROW Field consultation Maximum basal area (SOILQ) 130 m2/ha

Number of recruitment per plot (in normal year) 1-20 In the event of the chablis and the beginning of Same value of the 2 BIRTH the simulation when the leaf area index of the 1-600 KIAMBRAM model plot is less than 1.0. Maximum age Same value of the Mortality increases if diameter increment is less 0.368 KIAMBRAM model than 0.1 cm/ year (suppression mortality)

3 KILL Chablis:

♦ Occurrence probability (tree fall probability) 0.001 Field consultation ♦ Probability of the mortality in the case of 0.5-0.75 chablis

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Appendix-V: Major component in the KIAMBRAM model

Symbol used in the KIAMBRAM Subroutine Description model

Dmax GROW Maximum diameter of the tree that can achieve in the whole life span. Hmax GROW Maximum height of the tree that can achieve in the whole life span. Maximum age of the tree that can alive is computed from Dmax, Hmax Agemax GROW and KILL and G. Growth factor derived from Dmax, Hmax and Agemax or maximum G GROW value of ∂diameter increment. Shade tolerance level: 1=Shade tolerance, Tol GROW 2=Intermediate and 3=Shade intolerance Seed phenology: 1=Regular (Species having a seed source each year), P1 BIRTH 2=Irregular (Species having a seed source one time during three years). Seed longevity: 1 (<1.5 months), 2 (>1.5 months to 3.0 months), 3 (>3 P2 BIRTH month to 1 year) and 4 (>1 year). Dispersal by bird: SWITCH-1 BIRTH Flag –TRUE if species needs bird for seed dispersal. Dispersal by wind: SWITCH-2 BIRTH Flag –TRUE if species needs wind for seed dispersal. Dispersal by gravity: SWITCH-3 BIRTH

Seed dispersal Flag –TRUE if species needs gravity for seed dispersal. Flag –TRUE if species needs mineral soil for establishment. SWITCH-4 BIRTH

Flag –TRUE if species needs raised soil for establishment. SWITCH-5 BIRTH

Soil required Soil required Separate strangler and host species. SWITCH-6 STRGLE