Above-Ground Biodiversity Assessment Working Group Summary Report 1996-99

ABOVE-GROUND BIODIVERSITY ASSESSMENT WORKING GROUP SUMMARY REPORT 1996-99

Impact of different land uses on biodiversity 1 Compiled by A.N. Gillison (Coordinator)

Part A: Executive summary

Part B: Above-ground, ecoregional benchmark surveys2

Part C: An Intensive Biodiversity Baseline Study in Jambi Province,Central Sumatra, Indonesia

1 Director, Center for Biodiversity Management, P.O. Box 120, Yungaburra, Queensland 4884, Australia email: [email protected] http://www.cbmglobe.org. At the time of printing (2000): Senior Associate Scientist, Center for International Forestry Research, P.O. Box 6596 JKPWB, Jakarta, 10065, Indonesia. 2 Datasets available from ASB website: http://www.asb.cgiar.org

Gillison, A.N. (Coordinator), 2000. Above ground biodiversity assessment working group summary report 1996-99: Impact of different land uses on biodiversity and social indicators. ASB Working Group Report, ICRAF, Nairobi, 160p. [on-line] URL: http://www.asb.cgiar.org/PDFwebdocs/ASB Biodiversity Report.pdf

This report is one of a series detailing results from the Alternatives to Slash-and-Burn (ASB) Programme, a system-wide initiative of the Consultative Group on International Agricultural Research (CGIAR). The ASB programme, initiated in 1994, seeks to reconcile agricultural production and development with mitigation of the adverse local and global environmental effects of deforestation. Research sites are located in humid tropical forest margins in Cameroon, Brazil, Peru, Indonesia and Thailand. The global coordination office is located at the headquarters of the World Agroforestry Centre (ICRAF).

Editor: Polly Ericksen Cover Design: Damary Odanga and Bainitus Alenga Text Layout: Joyce Kasyoki Printers: Signal Press Cover photo: Debra Lodoen

Printed August, 2000.

For further information contact: ASB Programme, ICRAF P.O. Box 30677, Nairobi, Kenya Tel: +254 20 722 4000 or + 1 650 833 6645 Fax: +254 20 7224001 or +1 650 833 6646 Website: http://www.asb.cgiar.org Email: [email protected]

ASB encourages free dissemination of its work when reproduction and use are for non- commercial purposes, provided all sources are acknowledged. ASB follows a policy of open, public access to its datasets. Part A: Executive summary

This report covers the contractual requirement of ASB to the Global Environment Facility (GEF) to meet Goal 2 “Assessment of the impact on biodiversity of different land uses” as outlined in the aims and objectives of Phase II2. It also meets the broader goals of the ASB consortium to explore the dynamic linkages among biodiversity, carbon sequestration and productivity for human needs. The approach has been to establish a series of ecoregional biophysical baselines to first identify and then to evaluate, via intensive field studies, some of the key predictive relationships among plant and animal species and functional types and the physical environment. The size of this task required that it be tackled at two levels: first, to identify broad distributional patterns of key plant groups along gradients of land use at the ecoregional scale, as these are usually closely associated with both plant and animal performance overall; and second, to explore finer scale patterns of both plant and animal performance along an intensive land-use gradient within a specified ecoregion. The assumption has been that the information derived from the intensive study could reveal indicators of biodiversity response to land use that could be extrapolated and subsequently tested within the broader spatial ecoregional framework. Once identified, such indicators would be examined to assess their potential use by managers and planners in ongoing assessment and monitoring of biodiversity and as an aid to decision support for adaptive management. While this report deals mainly with above-ground biodiversity according to the TOR of the GEF contract, close attention has been given as well to below-ground elements in the intensive study in order to better understand the dynamic between biodiversity and land use. Funding for additional survey work in Cameroon and Indonesia was supplied by DANIDA and more recent, ongoing work exploring linkages between biodiversity and profitability in Thailand and Indonesia has been funded by ACIAR.

The study was conducted along a series of land-use gradients in the Western Amazon basin, Cameroon, Thailand and Sumatra, Indonesia. For each study area, digital elevation models were constructed and coupled with all available biophysical information relating to climate, land use, land cover, geology, soils, road and stream networks, and human population distribution. Clusters of sites were then located to span environmental variability to the extent of available logistic support. In-country teams of ASB partners then collected field data using a rapid survey technique for vegetation and site physical features. In the intensive study, the same technique was supplemented by recording associated animal and additional physical data, including soil physico-chemical attributes and above-ground carbon. This gradient-based approach produced several new outcomes that are of both scientific and practical significance and extend beyond the immediate GEF requirements. For the first time, the combined use of plant taxonomic and plant functional attributes (PFAs) has shown marked improvement in our capacity to predict biophysical response and thus, biodiversity, to land use impact. These response characteristics include (a) richness patterns in certain key plant and animal groups, (b) above-ground carbon and (c) soil nutrient availability – (and, by association, productivity potential for human needs).

From this point of view the study has met the needs of the GEF TOR in that we have established an improved theoretical and empirical basis for forecasting the impact of land use on biodoversity along defined environmental gradients. This now has the capacity to be translated into a toolkit for managers to rapidly assess the comparative biodiversity status quo within tropical, lowland forested and agroforested landscapes and their land-use potential and

2 Under GEF grant for Phase II of the ASB project.

1 then to use the acquired information to select appropriate options for sustainable management. In the course of data analyses and, at the request of the ASB social scientists, a vegetation index (the “V” index) was derived from a minimum set of plant-based variables known to be highly correlated with land-use type, plant and animal richness and soil nutrient availability. Together with a species/plant functional type-richness ratio measure, this has been incorporated into a Policy Analysis Matrix and into a bioeconomic simulation model developed by ASB partners. Recent surveys in Northern Thailand and Central and South Sumatra have established a framework for exploring linkages between biodiversity and profitability (total factor productivity). These suggest that correlations between readily observable, plant-based attributes and soil nutrient availability may be closely associated with, and thus predictive of, productivity for human needs. Information of this kind may be vital in generating and testing models of options for appropriate trade-off between biodiversity and profitability. The studies provide, for the first time, a scientific basis for generating and testing hypotheses about the role of biodiversity in productivity and related profitability in tropical forested lands.

A significant finding from the survey is that in a number of cases, plant and animal richness in early fallows and in late secondary forests and agroforests (especially jungle rubber and jungle Cacao) may exceed that of nearby intact or old-growth forest. The implications from this are that, together with certain fallow systems, certain agroforests can contribute significantly to overall biodiversity at the landscape level. The study also suggests that the influence of so- called dominant fallow weeds, e.g. Chromolaena and Lantana, should be investigated more closely, as in certain circumstances they may contribute rather than detract from biodiversity and soil nutrition. Highly significant correlations between certain sets of readily measurable, plant-based attributes and above-ground carbon suggest that the rapid survey approach may be appropriate for rapid assessments of above-ground carbon, and, under certain circumstances, below-ground carbon, where this is required to estimate actual and potential rates of carbon sequestration under different land use scenarios.

The methods used in the study have also provided a new and wholly quantitative, generic technique for profiling plant and animal habitats. Initial comparisons between similar land-use types in different countries reveal relatively consistent profiles when this method is used. A testable, cost-efficient, generic tool is now available for rapidly comparing ecosystem response in different parts of the lowland tropics where, for example, in similar physical environments in which species differ, plants and animals may possess similar adaptive traits. This development underscores the generally poor predictive capacity of plant species alone and without the benefit of complementary functional types. It also highlights the importance of including the genetically-based functional component of both plants and animals in the biodiversity equation. For management, this is significant as it opens the way to characterising individuals using non- species-based methods. For example, more than one plant functional type may occur within a species and vice versa. It is clear that biodiversity assessment cannot be meaningfully implemented in isolation from other landscape facets. Because many taxa and functional types range across different land-use types, biodiversity must be assessed within the context of gradients of land-use intensity and type. The predictive models and plant-based indicators are specific only for tropical, lowland environments; further study is required before these can be developed and tested for both lowland and upland ecosystems.

Specific software was developed in the course of the project to support both potential mapping of species (DOMAIN) and field collection and analysis of data acquired using the rapid survey vegetation proforma (PFAPro). The DOMAIN mapping software originally developed by CSIRO has been re-compiled to run under a user-friendly Windows environment. Both

2 software programs have been extensively used in training programs for above-ground biodiversity assessment in the three ecoregions. They are available gratis via the CIFOR web page and registered downloads of the DOMAIN program have been recorded from 45 countries since its release in August 1997. Although the PFAPro package has been used widely in both training and field operations, it is still in the beta-testing phase and new upgrades, including multilingual versions, are planned pending adequate funding.

Apart from the development of new biodiversity assessment survey tools, a significant outcome of the study thus far is the evident need for improved coordination of multidisciplinary field activities, in particular, the co-location of study sites. Despite early setbacks in planning and coordination of Phases I and II, the ASB program has now developed a very robust and productive research structure where devolution of research responsibility to in-country teams and partners is proving highly profitable. It is very evident from the dynamism inherent in almost all phases of land use in tropical forested lands that there is no one set of universal ‘best bet’ alternatives to slash and burn. Phase II has clearly shown that, at best, simple, generic tools based on sound scientific principles are likely to be more appropriate for managers to rapidly and cost-effectively assess and monitor the natural resource base than ‘one-hit’ land-use prescriptions developed in areas remote from the situation at hand. In a climate of largely unpredictable and stochastic biophysical and economic events, it will be necessary to have access to such tools in order to assess, compare, analyse and implement management with a greater awareness of biodiversity impact and economic outcomes. Research that targets such needs will produce a more efficient ‘bang for the buck’ by allowing managers and land owners to adapt to changing circumstance and to better control their livelihoods.

Future needs include a re-definition of research targets and a careful examination of knowledge gaps. The results of the above-ground biodiversity study suggest the methodology is now reasonably well worked out and requires further testing in upland and coastal environments as well as wetlands. While much more remains to be studied in the context of biodiversity and profitability, the recent improvements in research coordination indicate the real future challenge for developing sustainable options for management lies in understanding the dynamic linkages between biophysical and socio-economic elements of land use in forested and agroforested landscapes. Provided we can gain some understanding of these processes, this will open the way to appropriate policy intervention.

3 Part B: Above-ground, ecoregional benchmark surveys

Personnel:

CIFOR: A.N. Gillison (coordinator), N. Liswanti (Res. Asst.)

Indonesia: BIOTROP: E. Purnama, Upik R. Wasrin; WWF-Indonesia; international specialists from UK, Malaysia, Australia Thailand: ICRAF: D. Thomas and staff; Royal Forest Department: Chiang Mai University Brasil: EMBRAPA: E. Muñoz Braz, Abadio, L. Rossi, C. Reynel Peru: ICRAF: D. Bandy, J. Alegré ; INIA: M. C. Peralta, A. Ricsé. Mexico: ICRAF: A. Snook; INIFAP: J.C. Polito Cameroon: IITA: S. Weise, S. Hauser; Univ. Yaounde: Z. Louis

1. INTRODUCTION

These surveys were conducted as part of the research program of Alternatives to Slash and Burn consortium. It was designed to address Goal 2 of ASB Phase II, to "Assess the impact on biodiversity of different land uses”. The extreme logistic constraints associated with the ecoregional baseline studies in different countries meant that detailed, replicative sampling of ecoregional gradients had to be replaced with an approach that was logistically feasible but, at the same time, could be used to adequately sample key patterns of land-use impact. Because the ASB program is highly multidisciplinary, it was important to co-locate study sites wherever possible. Although sampling strategies differed between disciplines, sites were centered around a common spatially-referenced sampling point (a 40 x 5m vegetation plot). Wide-ranging surveys along distances of several hundred kilometers in some cases meant that sampling was often superficial, resulting in frequently poor correlates between different data sets. In areas without an effective calibrational baseline study, it was, therefore, not possible to establish any useful models of the impact of land use on biodiversity. Another major constraint was the lack of an acceptable operational definition of biodiversity. At the time of this study there was no model or sampling system that was available to help identify useful predictors of change in biodiversity due to land use.

For the purposes of this study, a two-tier approach was selected. The first approach aimed to select, as far as possible, a representative range of land-use types in each of four ecoregional benchmark areas (Western Amazon Basin Brazil and Peru, Sumatra, Thailand and Cameroon). In each of these study locations, a rapid survey was conducted along regional gradients of land- use intensity using a vegetation and site proforma to characterise key aspects of land cover. The assumption was that vegetation would reflect overall patterns of biodiversity. Within this broad framework, a second tier of sites were placed with a much more intensive study at a finer environmental scale, employing a range of above-ground animal groups as well as an examination of soil physico-chemical variables and soil macrofauna. The relationships derived between patterns of biodiversity and the physical environment at this fine scale are now being extrapolated at a wider ecoregional scale, using the spatio-environmental framework acquired using the first tier approach. From these two approaches, an attempt was made to identify

4 indicators that can be used to forecast the impact of land use on biodiversity and thus provide a basis for decision making for adaptive management under changing circumstances. Because of the highly complex dynamics of landscape management and different ecoregional cropping systems, indicators have been derived that are not based so much on species but rather on adaptive features of individuals – in the present case, plants. Such indicators can now be translated into species equivalents for specific regional and local conditions. For example, the jungle rubber ‘best bet’ in Sumatra might be equivalent to ‘jungle cocoa’ in Cameroon or Brazil in terms of similar richness patterns in plant species and functional types, but it may not necessarily have the same taxonomic composition.

2. INPUTS

2.1 Research aims:

• To develop a cost-efficient method for the rapid survey of above-ground plant biodiversity. • To identify, calibrate and test cost-efficient indicators of biodiversity for use in rapid survey and monitoring at the landscape level. • To identify linkages among biodiversity, soil nutrient availability, above-ground carbon and profitability and related impacts of different land-use practices. • To develop testable, analytical models to couple above and below-ground biodiversity with respect to the above. • To provide a scientific basis for cost efficient toolkits that can be used by managers and planners to acquire data about biodiversity as an aid for decision support for adaptive management and sustainable alternatives to slash and burn.

2.2 Summary of survey design criteria and field methods:

Purposive selection of biophysical gradients or benchmark sites was applied in three continental ecoregions. These covered a wide range of land use types using a gradsect- based survey design3 that employs as a sampling framework, those key environmental gradients that are either known or assumed to determine plant and animal distribution. At each site, the same procedure was used to collect biophysical data including site physical characteristics, vegetation structure, all vascular plant species and unique functional groups (modi) in a 40 x 5m plot. At each location, after consultation with in- country partners, a representative gradient of land-use types was selected for study. These were co-located as far as possible with other studies of below-ground biodiversity, carbon stocks and greenhouse gases. Each ecoregional benchmark contained a similar range of land uses from closed forest, tree crops, subsistence gardens and degraded grassland or pasture. While most surveys were necessarily superficial due to logistic constraints, they were supplemented by an intensive baseline study in Jambi province, Central Sumatra. In that study, a team of national and international specialists in plant and animal survey collected data that could be used to seek underlying patterns of plant and animal distributions along a readily distinguishable land-use gradient. The results from that survey (see Part C) have enabled the identification of the most efficient indicators of biodiversity habitat and provided the necessary platform for extrapolative mapping of key species and

3 Gillison and Brewer (1985). The use of gradient-based transects or gradsects in natural resource surveys. J. Environ. Manage. 20, 103-127.; Wessels, K.J., Van Jaarsveld, A.S., Grimbeek, J.D. and Van der Linde, M.J. (1998). An evaluation of the gradsect biological survey method. Biod. Conserv. 7, 1093-1121.

5 functional groups. In Mae Chaem, Northern Thailand (see Part D) plants and birds were sampled along land-use intensity gradients with mixed results, suggesting the need for a re-examination of the sampling technique for birds. The Cameroon study (Part E) was restricted to plants due to logistic and funding constraints. Overall, these indicators facilitated the analysis and interpretation of limited data from the non-intensive study sites. Details of the plant-based methodology can be found in the report of the 1997 Sixth Annual Review Meeting for ASB. Techniques for sampling animals are explained in the relevant surveys (Parts C and D) In order to assess the value of new locations, CIFOR supported the reconnaissance of additional sites in Mexico and Madagascar.

2.3 Above - and below-ground site locations and summaries:

Of the 162 plots located for above-ground (AG) biodiversity assessment, 92 were co- located with below-ground (BG) sites (including 16 intensive baseline plots from Sumatra). A wide range of land-use types from pasture to different agroforestry to forestry systems were sampled in Brazil (21 plots in Rondônia/Acre), Perú (36 plots in Pucallpa and Yurimaguas); Yucatan (9 plots in Zona Maya and Campeche); Indonesia (47 plots, Jambi); Cameroon (21 plots Mbalmayo/ Yaounde). (Annex 1, Table 2); Thailand (28, Part D). For carbon stocks and greenhouse gases, 50 sites were co-located (see Annex II, Figure 1a and refer Climate Change WG report).

2.4 Data storage, distribution and access:

Data were recorded in hard copy in the field and later transcribed into a computer using the FUNDAT computer program developed in CIFOR (now replaced with the more recently developed PFAPro). All data were spatially referenced using a Global Positioning System and stored in a Microsoft Access (*.mdb) format. In this way, data from all benchmark sites can be accessed in a uniform way and analysed both separately and in toto. This method of data acquisition was developed after close consultation with field teams in each country. Team representatives from each country were supplied with a set of diskettes, each containing a complete data set from all ecoregional sites. The core data set has been archived in magnetic media and hard copy at CIFOR, Bogor, Indonesia. Additional backups have been made on 3.5” diskettes and IOMEGA ZIP diskettes. Graphic files and data catalogues have been transferred to the office of the ASB Coordinator in Nairobi. A comprehensive data catalog is available in Annex III Table 13. Data from the recent Thailand study are yet to be included in this catalogue (but see Part D for details).

2.5 Analytical methods and models:

Preliminary regression and multivariate cluster analyses (multi-dimensional scaling) have been applied to data from all sites. Limited spatial modeling of the potential distribution of plant species richness has been completed for Jambi province, Central Sumatra. At present, all data analyses are being conducted at CIFOR.

6 3. OUTPUTS:

The following items highlight some of the key outputs of Phase II thus far. A more comprehensive list of achievements both in line with and outside the GEF contract is outlined in Tables 1 & 2 below.

3.1 Above-ground pattern of plant taxonomic and functional features within and between benchmark sites:

There is a consistent trend across all benchmarks between the pattern of plant functional types and types of land use. Richness in vascular plant species and richness in plant functional groups are highly correlated. [See report on ‘V” index and conclusions below.]

3.2 Indicators of above-and below-ground taxonomic and functional or trophic groups:

Taxonomic identification of above and below-ground collections of fauna have been completed. A major constraint to the selection of useful correlates is the wide variation in range distribution and ecological behavior between different groups of biota such as mammals, birds, plants and microfauna in cryptic habitats. It is no surprise, therefore, that there are no obvious linear trends between, for example, nematodes (see Below- Ground report) and plant species or plant functional richness. For the many cryptic fauna the lack of taxonomic identification has meant closer reliance on characterisation of functional or trophic groups.

Nonetheless, several key plant-based indicators have emerged from the multidisciplinary baseline study conducted in Sumatra. Two examples drawn from termites and birds illustrate the potential value of plant vascular species richness and richness of plant functional types as indicators of these groups. An intensive, co-located study of ground-dwelling termites and vegetation plots revealed significant correlations between termite species richness, plant species richness and plant functional type richness. The highest correlation was found by using the ratio of plant species richness to plant functional type richness. Using this ratio measure a similar improvement was found for above-ground carbon, Collembola, Termites and Birds (see Part C, Annex II, Figures 1a,b,c,d). The ecological explanations for these correlates are not entirely clear but the results suggest that at least some animal groups may be responding to gradients of overall heterogeneity of vegetation, expressed as a function of variability in both plant taxa and functional types and reflected in soil nutrient availability. This finding is a new and promising tool for estimating species richness in certain key groups such as the 'ecosystem engineers' represented by termites.

The combined use of richness values in plant species, plant functional types (or modi) and the species/modi ratio can also be used to characterise a profile using cumulative values along 5x5m quadrats of each 40x5m transect (Annex I, Figure 1).

3.3 Linkages between soil substrate, land-use pattern and above-ground biodiversity:

A highly significant, statistical relationship (r2 >95%) between plant species richness and plant functional types (PFTs or modi) can be related to gradients of land use in

7 Jambi. Annex I, Figure 7 illustrates the pattern derived using multi-dimensional scaling of Plant Functional Attributes (Species-weighted summary of individual elements of PFTs) with Land Use Type overlays in which a suggested area of ‘best bets’ is identified. At least for the Jambi baseline study along land use patterns in differing soil conditions, highly significant correlations have been established between PFTs, plant basal area, mean canopy height and certain soil attributes such as bulk density, total soil nitrogen, cation exchange capacity and pH. These attributes provide key indicators of land use impact on site biodiversity and productivity for human needs. Annex I, Figure 3 illustrates a close predictive relationship between patterns of richness in plant species, plant functional types and land-use types. While the correlations are not so high for the Mae Chaem study in Northern Thailand, they are nevertheless significant (Part D).

3.4 Linkages between greenhouse gases, carbon stocks and above-ground biodiversity:

Data analyses of GHGs are yet to be finalised. A total of 50 AG plots were co-located with those of the carbon sequestration group. (Cheryl Palm). The data have been analysed for correlates between certain plant variables and above-ground carbon. (Annex I, Figure 2). While there is no clear statistical relationship (ref: Climate Change WG Report) there are obvious patterns between AG carbon stocks and land-use types and associated PFTs. Data acquired from the intensive Jambi baseline study have revealed a significant correlation between carbon stocks (Hairiah and van Noordwijk, Sect.10 Part C) and the ratio of plant species richness to PFT richness. (Annex II, Figure 1a)

3.5 The “V” Index: A potential indicator of land use impact on biodiversity and profitability, based on key vegetation structural, plant taxonomic and functional types (PFTs):

From the Sumatra baseline study, it has been possible to extract key vegetation indicators of impacts of land use and environmental change. These are: mean canopy height, basal area (m2 ha-1), total vascular plant species, total PFTs or functional modi and a ratio of plant species richness to PFT richness. Multi-dimensional scaling analysis (available in a wide range of exploratory data analysis packages) can be used to extract the single best set of values (eigenvector scores) for a specified set of sites characterised according to these variables. When standarised, the values can be used as a relative index of vegetation that, in the present study, corresponds closely with observed impacts of land use on biodiversity, crop production and associated ‘time since opening’ (e.g. clearing for cropping or harvesting). (Annex I, Figures. 4,5,6). This set of values is termed a “V” index. While there are close correspondences with plant and animal biodiversity, the V index is more a habitat or site characterisation indicator than an actual index of biodiversity. The method can be used at any scale, thus facilitating the comparative analysis of site data for scaling-up or scaling-down purposes. While the index is relatively crude and very simplistic, it has the novel advantage that, because of the high correlation with observed land use pattern, it may be a useful variable in econometric models (e.g. FALABEM – S. Vosti pers. comm.) and in assessing potential ‘profitability’ (total factor productivity) for a specified land use or set of land uses within a landscape context. (See 3.6 below). CIFOR has acquired funds to address this possibility in association with ASB (Ref: successful bid to ACIAR re: ‘Above-ground biodiversity and productivity assessment for Alternatives to Slash and Burn;’ (A.Gillison, T.Tomich and D. Thomas) and this is currently being investigated

8 in Northern Thailand (Mae Chaem) and Central and South Sumatra (Lampung). The multi-dimensional scaling approach used to derive the “V” index can also be used to graphically display a zone of “best bet” characteristics for which specific values of plant-based variables can be identified (Annex I, Figure 7).

3.6 Linkages with the developing ASB Policy Analysis Matrix:

There are few established criteria for identifying appropriate vegetational or other biodiversity-based variables that can be used directly to help construct a meaningful policy analysis matrix (PAM). Because the five vegetational variables used to construct the ‘V’ index above appear to correlate better than others with faunal habitat, soil nutrients and carbon stocks, they may be useful albeit, indirect, indicators of profitability and agronomic sustainability (see other WG reports). For this reason complete data sets have been generated for each of the benchmark sites and added to the PAM currently under development (ref: separate input by D. Thomas, T. Tomich, S. Vosti and J. Witcover).

3.7 Thematic maps of biodiversity pattern in central lowland Sumatra and progress in spatial data compilation in Latin America, Cameroon and Thailand:

Digital, spatially-referenced data are essential for constructing spatial models and for providing a common multidisciplinary platform for multidisciplinary, scientific discussion. A digital elevation model (DEM) has been completed for Jambi, Sumatra and Pucallpa, Peru. An additional DEM for the Mbalmayo transect in Cameroon was extended by about 200 km to include the northern savannas near Bafia (05°. 02’. 40” N.Lat.). Electronic media copies of the DEM and other spatially-referenced data have been placed with each country partner. The DOMAIN potential mapping software program has been used to generate preliminary maps of biodiversity pattern in Sumatra (Jambi). Unlike the other ecoregional sites, the Jambi study includes data acquired from CIFOR's additional sites throughout Central Sumatra (about 147 in all). The ICRAF team in Chiang Mai has produced a comprehensive DEM of the Mae Chaem watershed (Part D Map 1). These provide an expanded context of land use gradients that will facilitate extrapolative modeling and field testing of biodiversity patterns at the landscape level.

3.8 Remote sensing applications:

Models of ecosystem behaviour, including the response of biodiversity to land use impact, can be developed via intensive baseline studies such as the one conducted in Jambi. However, their use is likely to be severely limited unless they can be readily extrapolated to the landscape level. For this reason, CIFOR has acquired both airborne and satellite radar imagery of the Jambi study area (via the Government of Indonesia and Netherlands Government INDREX program). These data are being investigated using ground-truthing methods based on the PFA proforma. Further studies are planned for the next phase of ASB, using high resolution SPOT image over a 2000 km2 template that includes the Jambi baseline location. Other sequential Landsat imagery has been acquired from 1983 to 1995 for lowland Jambi to explore patterns of forest retreat associated with documented land use. These data are also being examined to ascertain their indicator value for above-ground (and possibly below-ground)

9 biodiversity. If indicators are detected then the spectral signatures may have value in extrapolating patterns derived from ground surveys and from thematic maps generated via DOMAIN. This activity is being developed further in association with GCTE and BIOTROP (ICSEA), as there are potential linkages with climate modeling.

3.9 Identified ‘best bet ’ alternatives to slash & burn:

For most locations, there will be no single ‘best bet’ alternative; but the methods described here can be used to help identify a range of options or ‘best bets’ for specified land uses. These aim at providing acceptable trade-offs between defined land uses and related profitability and their impacts on biodiversity. Overall, these were represented by the richest multistrata agroforestry plots. In the case of Brazil these were plots in ‘Nueva California’ that were composed of Peach Palm, Acerola, Cupuaçu, Coffee and other minor crops. In Sumatra, indications are ‘jungle rubber’ is a preferred option, whereas in Cameroon it is ‘jungle Cacao’ . It is important to note that although as many representative land use types were sampled, there were significant omissions, for example rubber and oil palm in Cameroon. Nonetheless, the same general principles appear to hold across all sites. The most depauperate plots for biodiversity and possibly some of the least productive were on Alang Alang (Imperata cylindrica) and Cassava (Manihota utilissima) in Jambi and Mbalmayo and degraded pastures in Perú and Brazil. While the value of establishing ‘high biodiversity’ complex agroforests is relatively clear, what is not clear is the ecological role of some early fallow systems such as those dominated by Chromolaena odorata and other ‘daisy fallows’ dominated by such genera as Baccharis and Vernonia. Preliminary results suggest these may play an important role in facilitating habitat rehabilitation under certain circumstances (see below).

3.10 Training workshops: Training workshops in above-ground biodiversity assessment techniques have been completed in all benchmark areas. For the Western Amazon Basin, a workshop was completed in Pucallpa Peru with 26 participants from Mexico, Bolivia, Brazil and Peru. In Cameroon a workshop was conducted in July 1997 with 21 participants and a regional S.E. Asian workshop with 26 participants from Indonesia, Malaysia, Thailand and the Philippines was completed in December 1997.

3.11 Publications and software:

3.11.1 Publications: Gillison, A.N. (1997). Mapping the potential distribution of plants and animals for wildlife management: The use of the DOMAIN software package. In: K. Romimoharto, S. Hartono and S.M. Soenarno (eds.). Proceedings of the National Seminar on The Role of Wildlife Conservation and its Ecosystem in National Development. pp. 114-119 + two maps. The Indonesian Wildlife Fund. (IWF), Jakarta. Gillison, A.N. and Carpenter, G. (1997). A plant functional attribute set and grammar for dynamic vegetation description and analysis. Functional Ecology 11, 775- 783. Gillison, A.N., Liswanti, N. and Arief-Rachman, I. (1996). Contributors. In: Final Report, Rapid Ecological Assessment in HPH Pt Serestra II and HPH Pt Bina

10 Samaktha. Pre-implementation, Integrated Conservation and Development Project, Kerinci Seblat National Park. World Wildlife Fund for Nature, Indonesia Program. Bappenas, The World Bank. (Published 1997). Gillison, A.N. et al... (1999) Overview report of the ASB Intensive biodiversity baseline study Nov.-Dec. 1997. Ca. 65 pp. 12 Tables, 5 Annexes and 4 Maps. CIFOR Working Paper (in prep.). Gillison, A.N. (1997). In: Catherine Kenyatta, ed. Summary report of the above-ground biodiversity working group. Annex II Alternatives to Slash and Burn, Report of the 6th Annual Review Meeting. 17-27 August 1997, Bogor, Indonesia. Pp. 52-64 (ICRAF, Nairobi). Vanclay, J.K., Gillison, A.N. and Keenan, R.J. (1997). Using plant functional attributes to quantify site productivity and growth patterns in mixed forests. Forest Ecology and Management 94, 149-163.

Note: At the time of writing several manuscripts have been submitted to scientific journals and more are in preparation with in-country co-authors.

2.11.2 Software: Carpenter, G. and Gillison, A.N. (1997) DOMAIN Version 1.3 for Windows. A software package for mapping the potential distribution of plants and animals [Since its availability on the CIFOR web page in August 1997, downloads have been registered from users in 45 countries] Carpenter, G. and Gillison, A.N. (1998,99). PFAPro – a data-entry and meta-analysis package for the PFA field proforma. Designed for field recording of site physical attributes, plant taxa and plant functional attributes. A second beta test version is available at time of writing [see above]. This package replaces the former FUNDAT package also developed by CIFOR in association with ASB.

Table 1

ABOVE-GROUND BIODIVERSITY ACHIEVEMENTS - 1996-99*

• Field protocols finalised, tested and disseminated at field and landscape levels in all benchmark sites [2.1.1] • Plant-based indicators for certain above- and below-ground faunal groups identified and others under study. [2.1.2] • Generic procedure determined to identify alternative best-bets (e.g complex agroforests) via an index derived from key vegetation variables. [2.2.1] • Spatially-referenced databases completed for all benchmark sites [2.2.2] • Methods established for selection of plants for sustainable enrichment. [2.3.1] • Implementation of field management practices for degraded lands not undertaken (models must be developed first) [2.3.2] • On-site training in assessment protocols completed for all benchmark sites and all ecoregional centers [2.4.1] • Preliminary training in the use of spatially-referenced data sets completed in South- East Asia [2.4.2] ------* GEF contracted Phase II activity in brackets.

Table 2

ABOVE-GROUND BIODIVERSITY ADDITIONAL ACTIVITIES - 1996-99*

• New measure of functional diversity and functional complexity (This complements the standard Shannon-Wiener and Simpson diversity indices commonly used for species: mss in preparation). • Preliminary fieldwork completed for Yucatan Peninsula (Campeche, Zona Maya) in S.E. Mexico; Sites identified for Thailand (Chiang Mai); Madagascar econnaissance completed in September 1997. • Intensive baseline study of above-ground plant and animal species in Jambi along a land use gradient completed in November 1997. • A recent study in Lampung, S. Sumatra has just been completed (Sept. 1999) exploring relationships among coffee-based agrosystems, biodiversity, profitability, above and below-ground carbon and soil nutrient availability. • An intensive, biodiversity baseline study was completed in Mae Chaem, Northern Thailand, exploring relationships between land use type, biodiversity (plants and birds), soil nutrient availability and profitability (July 1999). • Digital Elevation Models completed for Indonesia, Peru (Pucallpa) and Cameroon. Brazil to be determined. Comprehensive DEMs are available for Mae Chaem via ICRAF, Chiang Mai. • ASB sites in Indonesia analysed within the context of other non-ASB, CIFOR ecoregional study sites. • Analysis of remotely-sensed imagery of Jambi Land Use Types (LUTs) (radar and Landsat) is underway.

12 • Computer-based software (DOMAIN) upgraded for potential mapping of plants and animals. Data-entry software (PFAPro) to support use of the Plant Functional Attribute proforma was completed in May 1998 with recent upgrades in August 1999. Applied at international training course in biodiversity assessment conducted by Smithsonian Institute & Man and Biosphere, USA.. Used in training courses in Cameroon, Thailand and Vietnam. • Preliminary development of common database format for all Working Groups has been established in Indonesia (ICRAF & CIFOR). • Initial spatial model of ‘zone of extrapolation’ completed for tropical regions based on benchmark studies (ref: extended WG report) ------* Most of these activities were funded directly by CIFOR and although complementary, were external to the GEF contract.

4. Findings thus far:

• Intensive baseline studies along gradients of land use are necessary to identify and calibrate biodiversity indicators. Sampling methods must be designed to accommodate, as far as possible, the highly complex interactions between biota and their physical environment. The data acquired and the predictive models developed must also be capable of extrapolation and verification at the landscape level. For these reasons it is necessary to first design a sampling structure that includes a representative range of land-use gradients in any region under study. Second, intensive, co-located studies of above- and below- ground biota and their abiotic determinants (soil, climate, land use, etc) are needed to construct initial correlative models of the distributional relationships of both along natural and modified resource gradients. These correlates can then be coupled with spatial models and patterns of biodiversity extrapolated for specific environments. Thematic maps of biodiversity pattern can then be tested via ground survey and remotely sensed imagery. Information acquired in this way can be used to frame process-based research where this is needed. The Phase II study has shown clearly that this is the very minimum required to develop a requisite knowledge base for constructing sustainable models of options for managing forested and agro-forested lands. Using this approach, a two-week, intensive multidisciplinary, baseline study conducted in Jambi, Central Sumatra produced far more effective information than 2.5 years of largely uncoordinated, rapid surveys of sites by different scientists across regional environmental gradients. In addition, the products of carefully designed, intensive, gradient-based studies are far more likely to generate more productive insights into ecosystem behaviour and publications in peer- reviewed scientific journals. With this in mind, the cost-efficiency of survey design should be a key consideration in planning and budgeting for future work.

• Biodiversity cannot be meaningfully estimated in terms of species alone. Species richness and composition must be coupled with functional richness and composition in order to better understand land-use impacts on farming and natural systems. While plant- based estimates of species richness are the most commonly used indicators of overall biodiversity pattern, predictive value may be significantly enhanced by using a ratio of plant species richness to functional group richness as an indicator.

13 • Isolated, single-point samples of biodiversity can be misleading. Biodiversity must be sampled within a representative range of key land-use types if dynamic models of land-use impact are to be developed. Knowledge of range distributions of key taxa and functional groups is critical to developing performance models and to estimating thresholds of sustainability.

• Peaks of richness in both species and functional groups do not necessarily occur in pristine forest. In lowland tropical humid vegetation, frequently occurring ‘highs’ are most likely to be found in late stage secondary forests and frequently on base-rich soils, especially in 1-3 year old ‘daisy fallows’ following slash and burn gardening. This finding tends to run counter to conventional concepts of richness patterns in vegetation where greatest richness is assumed to be in tropical, humid, lowland rainforest.

• The importance of early fallows dominated by members of the Asteraceae (Compositae) (here termed the ‘Daisy fallow’) may be seriously underestimated in terms of their associated biodiversity value and contribution to nutrient pools, soil structural improvement and ecosystem dynamics. Mayan agriculture treats these fallows as highly significant components in overall land-use planning. Results across all ASB benchmarks seem to confirm that the ‘daisy fallow’ (variously dominated by large Asteraceae, Baccharis, Chromolaena (Eupatorium), Tithonia, Vernonia etc.) should be considered potentially beneficial in agroforest ‘best bets’. Priority research is indicated to examine the impact of their inclusion and exclusion in agroforestry systems.

• Indicators of ‘best bet’ agroforests. Records of total vascular plant species and unique plant functional types or modi collected via the rapid survey technique can be used to estimate ‘best bet’ alternatives by identifying those conditions where species and functional richness are maximised. When matched against a newly developed index that characterises plant functional groupings per plot, a highly robust statistical model can be used to compare values of plant biodiversity in terms of species and functional richness.

• More complex estimates of plant functional diversity and plant functional complexity that compare evenness and composition of groups. These have already been developed by CIFOR for forests and will be applied to the ASB sites.

• New global records of plant species and functional richness: Data collected to date from the tropical lowland agroforested landscapes of Cameroon, the Western Amazon and Indonesia reveal some consistent trends. While some Cameroonian forests are relatively rich (50 -100 vascular plant species per 40x5m plot), they tended to be poorer than those in the Western Amazon basin (typically 70 –100 per plot), which in turn fall well below many in Sumatra that frequently exceed 150 per plot. Density patterns appear to vary with disturbance history and type of manipulation. While in mature, relatively undisturbed forests, individuals and species may be relatively widely spaced, a managed ‘Durian’ forest in lowland Sumatra revealed a staggering 62 woody vascular plant species in the first 5x5 m of a 40x5m plot. This is by far the highest species density record documented thus far for all forest types in the new and old-world tropics using this recording technique. Density of species and individuals usually varies with the nature and frequency of disturbance. This phenomenon may have implications for ecosystem management that could differ considerably from that developed for better known forested landscapes with far fewer species.

14 • Correlations between plant-based attributes and above-ground carbon and soil nutrient availability: In the Sumatran study, high correlations have been established between sets of plant-based features, especially those involving Plant Functional types, certain vegetation structural attributes, and above-ground carbon and soil nutrients. These highly predictable relationships and the relative ease of measurement of the plant-based features suggests there may be a potentially useful set of indicators for rapid assessment of carbon stocks where this is required in complex, tropical forested landscapes. In the Thailand study, soil nutrients were less well-correlated with vegetation and avifauna. The Phase II studies have at least provided a valid basis for testing hypothetical relationships between carbon dynamics and land use that may be relevant for scaling up for climate modeling purposes. The high correlation with soil nutrients indicates this will be potentially useful in estimates of productivity for human needs expressed as profitability.

5. Gaps in knowledge; funding priorities; training needs:

• Additional study sites and funding support are needed to provide a more comprehensive knowledge base for developing models of sustainable management for biodiversity especially that related to estimating trade-offs for profitability. (See Table 3.) • The extrapolative capacity from existing and future sites can be examined via the use of DOMAIN software. (Annex I, Figures 8, 9) • Funding priority should be given to intensive, multidisciplinary, ecoregional baseline studies rather than to independently coordinated research activities. • A large knowledge gap in a global understanding of the generic potential of output from the ASB project requires a more uniform approach to ecoregional methodology involving data acquisition, data storage and the development of spatial models. • Training of trainers to facilitate technology transfer. • Follow-up training for in-country teams, in particular training in elementary data analysis and spatial modeling. • Multi-lingual manuals for using the field proforma and the associated data-entry software programs. • Electronic networking to facilitate transfer and analysis of data. • A need for national agencies and NGOs to be more self-reliant in field operations, data analysis and interpretation and in advising management on best bet options.

Table 3

SUGGESTED ADDITIONS TO BENCHMARK SITES: PHASES II & III

EXISTING BENCHMARKS (Extensions to Phase II)

Indonesia: Additional sites to include wetland and upland sites and fallow systems (?30). Funds permitting, a second intensive baseline study in upland Jambi (ca. 1500m a.s.l.) Cameroon: Additional 35 sites in the now extended Mbalmayo transect to Makam III Peru: Additional 55 sites to create a transect from Iquitos through Tarapoto to Chanchamayo. Brazil: Additional 13 sites extending the present Rondonia – Acré transect to include additional fallow systems and community managed forests. Thailand : Mae Chem watershed with seasonal savannas and Pinus forests.

PROPOSED NEW BENCHMARKS (Phase III)

Mexico : Yucatan – Petén Africa : Madagascar: Ranomafana and Masoala Nigeria and Ghana (Proposed EPHTA sites)

6. Future needs – Phase III 6.1 A more systematic approach to site location and intensive, co-located multidisciplinary studies along key environmental gradients: As argued in 4. above, there is a need for a more intensive research focus to better understand the interrelationships between biodiversity and land-use impact. Using global, public domain data, spatial models of pantropical gradients of climate and resource substrate can be readily used to identify gaps in the knowledge base. Rather than have loosely coordinated working groups operating more-or-less independently, it would be far more cost-effective to focus joint activities in a common resource area and with a clear perspective of the research problem at hand. Such intensive activity will require a higher-than-usual funding and logistic support per unit time but would result in fast turnaround of outputs, publications and technology transfer with a better capacity for research coordination and communication than existed in the earlier phases of ASB. 6.2 Additional features to existing methods of characterisation: The results of studies so far indicate that the characterisation of sites by vegetation alone may be insufficient for studies of land use impact on biodiversity. Additional, parallel surveys of key animal indicators (e.g. birds) may need to be more carefully investigated. Further studies of plant-based indicators of carbon dynamics may require a re-examination of the minimum attribute set currently applied via the rapid survey vegetation proforma. 6.3 Synthesis and models: Considerable uncertainty surrounds the synthesis of existing AG and BG data and the development of synthetic models that can be used to provide a set of options for sustainable management. A prime need is the standardisation of data collection and data storage, and there remains a need for a data hub within the ASB consortium that would serve as a common platform for data access. The recent synthesis meetings in Nairobi and Brasilia have highlighted the need for a cross-disciplinary framework for developing and testing models of sustainable land use at the landscape level. A fundamental aspect of this development will be the continuing need for baseline studies that can be used to parameterise and calibrate such models. Output will need to be carefully examined to ensure a seamless connection with predictive spatial models. 6.4 New Benchmark sites: As listed in Table 3, proposed new benchmarks include the sites targeted by EPHTA (ref. Stephan Weise), Madagascar (A. Gillison) and Thailand (D. Thomas). These additions will be necessary to achieve requisite representativeness of tropical regions and agroecological zones. At the time of writing, reconnaissance of Madagascar has been completed (A. Gillison), in addition to which, data from agroforestry systems in Papua New Guinea have been obtained using the standard vegetation assessment techniques. The results from the Mae Chaem study in Thailand indicate that a more representative set of land use types is needed for that ecoregion.

6.5 Training the trainers: With the expansion of activities in biodiversity assessment it will become necessary to train teams and, in particular, team leaders who can pass on the necessary technology. Whereas the previous series of workshops were successful, it will be more efficient to undertake longer and more intensive training sessions with fewer personnel. While vegetation aspects of AG are generally adequate, these new training sessions might require additional features to include assessment of productivity for human needs. Such additions would require new training techniques. It is planned to conduct a joint training course in Indonesia in late 1999 (T. Tomich pers. comm.) in order to provide potential managers with an integrated approach to natural resource assessment that combines both the socio-economic and biophysical components.

Part C: An Intensive Biodiversity Baseline Study in Jambi Province,Central Sumatra, Indonesia

Preliminary Report

Compiled by A.N. Gillison and N.Liswanti1

Contents

Section 1. Summary and overview 2. Rapid vegetation survey 3. Vegetation and land use types 4. Birds 5. Mammals 6. Canopy insects 7. Soil macrofauna 8. Terrestrial insects 9. Land snails 10. Soil and aboveground carbon 11. Preliminary synthesis 12. Annexes

1 Center for International Forestry Research P.O. Box 6596 JKPWB Jakarta 10065, INDONESIA Email [email protected]; [email protected]

SECTION 1: SUMMARY AND OVERVIEW

1.1 Summary:

This section reports the preliminary results of an intensive biodiversity baseline study that was undertaken to establish an improved scientific basis for selecting indicators for biodiversity assessment. The sampling framework centred around a series of 16 (40x5m) plots that were established along a gradient of increasing land-use intensity. While these plots were designed for a vegetation survey, they formed a focal point for co-located surveys of various animal groups and analyses of soil physio-chemical properties and above-ground carbon. A team of 23 national and international specialists in biodiversity assessment undertook the survey in an area of lowland Sumatra that included land-use types ranging from intact rain forest through various logged-over and secondary forests, mixed agroforests and plantations, to degraded grasslands. The survey produced several outcomes that are significant to both science and management. These include the identification of a greatly improved set of plant-based indicators of biodiversity, soil nutrient status and above-ground carbon. The indicators are based on combinations of richness of vascular plant species, Plant Functional Types (PFT) and a ratio of species richness to PFT richness. To these can be added vegetation structure (mean canopy height, basal area in square meters per hectare) that improve the prediction of certain site physical features, and some animal taxa. The combination of plant species, functional types and structure can also be used to generate an overall vegetation index (the “V” index outlined in Part B) that is itself highly correlated with various animal taxa and site physical conditions. A statistical analysis shows that many correlations are non-linear, maximum variance being accounted for by second order polynomial regressions. Exploratory data analysis confirmed that specific combinations of these indicators can be used to identify ‘best bet’ conditions such as jungle rubber, where biodiversity (expressed as richness of taxa and functional types) may in some cases exceed that of pristine forest. The study has established a scientific basis for exploring linkages between plant and animal taxa and functional types, soil nutrition (and thereby potential site productivity) and carbon sequestration. The results provide a ready means of approximating biodiversity patterns across a range of land-use types that typify much of the lowland tropics around the world. This illustrates how plant and animal species richness varies with land use impact. This information provides an improved basis for forecasting the impact on biodiversity of forest conversion to different land uses. The methodology can be readily adapted for use by management where rapid assessment of site conditions is needed, and where site-based information is critical to support adaptive management under changing environmental and socio-economic conditions. The information acquired at this sub-regional scale is generally consistent with that for similar land-use types in other countries. Spatial extrapolation of biodiversity patterns can be readily tested using DOMAIN potential mapping software and the digital environmental data acquired for the Sumatran benchmark site. The survey has generated a series of scientific papers authored by national and international participants. The study also provided invaluable material for case studies that are being included in a multi-media training manual for rapid vegetation assessment as a component of biodiversity.

1.2 Introduction:

This survey was conducted as part of the research program of the ASB consortium. It was designed to address Goal 2 of Phase II of ASB, which is to "Assess the impact of different land- use practices on biodiversity". The extreme logistic constraints associated with the ecoregional baseline studies in different countries meant detailed, replicative sampling of ecoregional

19 gradients had to be replaced by an approach that would be logistically acceptable but at the same time could adequately sample key patterns of land use impact. (See survey design below). Because the ASB program is highly multidisciplinary, it was important to co-locate study sites wherever possible. Although sampling strategies differed between disciplines, sites were centred around a common spatially-referenced sampling point (a 40 x 5m vegetation plot). Wide-ranging surveys along several hundred kilometers meant sampling was often superficial, resulting in frequently poor correlates between different data sets. In the absence of an effective calibrational baseline study, it was therefore not possible to establish any useful models of the impact of land use on biodiversity. Another major constraint was the lack of an acceptable operational definition of biodiversity. At the time of this study there was no model or sampling system that was available to help identify useful predictors of change in biodiversity due to land use.

It was clear that in order to develop any useful, testable model of land use impact on biodiversity, the ASB above - and below-ground teams had to start from scratch. Sumatra is known to contain some of the world's highest levels of richness in plant and animal species. Unfortunately, it is suffering major impacts from poorly planned land use arising from land clearing for oil palm and rubber plantations. Because these conflicts typify much of the lowland tropics and because information was already available from earlier CIFOR surveys of representative Land Use Types (LUTs) (A.N. Gillison and N. Liswanti), Sumatra was chosen as the focal area for an intensive biodiversity baseline study. The aim was to first locate a representative gradient mix of LUTs and physical environments and second to sample these according to site physical characteristics, specific vegetation features designed to reflect taxonomic variability as well as adaptive features, and a range of animal taxa (birds, mammals, insects, molluscs). It was assumed the resulting data sets would be adequate for developing testable models of plant and animal response to land-use impact. This procedure would help identify indicators for use in subsequent rapid assessments of impact in similar tropical lowland forested landscapes, thus reducing the need for intensive and costly surveys.

Without some ready means of extrapolating (mapping) findings, results from any survey are of limited use for management. An important focus for this operation was to ensure all data were spatially referenced as accurately as possible. High quality GPS readings (Trimble Scoutmaster using the Acculock system) were obtained mostly with a conservative accuracy of ± 70m. The aim of this approach was to establish adequate spatial data for modeling the potential distribution of plants and animals under different LUTs and physical environments. If shown to be successful, such models would be potentially useful for coupling biophysical interactions with socio-economic models being developed by other ASB groups. It is assumed that by constructing integrated models of biophysical-socio-economic interactions it will become possible to generate options for adaptive management to cope with unexpected variations in climate and market forces triggered, for example, by episodic El Niño and La Niña events.

Multidisciplinary surveys are costly in time, money and coordination. If carefully designed, they can be enormously cost-effective. Forward planning is essential in order to acquire the right mix of international and national specialist for the different plant and animal groups. Planning for the present survey began a year before, and extensive reconnaissance was needed to establish the most suitable location. The assistance of BIOTROP was sought initially, as this Indonesian-based NARS possessed a research station centred in lowland Jambi Province in Central Sumatra with adequate accommodation and electrical power to serve most of the needs of different specialists. Further, CIFOR had established a close working relationship with life- scientists from BIOTROP and ICSEA. For biodiversity surveys, timing is critical as seasonal

20 variations can have a major effect on the nature of the data collected for different plant and animal taxa. Towards the scheduled start of the survey, Jambi was gripped in an El Niño drought that threatened a postponement to the following year. Fortunately a weather change with heavy rain ten days beforehand created near-perfect conditions for a baseline survey. The field operation was conducted between 16/11/97 and 2/12/97. Most taxonomic identifications were completed by mid-1998 via contracts arranged through research institutions in the UK and Australia.

1.3 Budget:

Complete costs are difficult to estimate given that certain salary costs of CIFOR and ICRAF staff and in-kind assistance from partner institutions are not included. The bulk of the in-field survey costs contract fees for specialists and subsequent contracts for taxonomic identification at various research institutes was approximately USD$98,000. Funding was covered in part through ASB (60%) with the remainder from USAID and DANIDA. In retrospect, given the results of the survey, the number and quality of the participants and the high level of infrastructure support, the operation could be regarded as relatively low-cost. A parallel study in more remote and less well supported lowland tropical region such as parts of Kalimantan or West Irian would have been twice as costly.

1.4 Participants:

A detailed list of participants is available in Annex 1, Table 3. A total of 27 scientists and support staff participated in the survey. International specialists were drawn from the British Museum of Natural History, the Institute for Terrestrial Ecology, UK, Oxford University (Depts of Geography and Plant Science), and the University of Malaysia. National scientists from Indonesia were from LIPI (Herbarium Bogoriense, Zoology Museum), SEAMEO BIOTROP, University of Brawijaya and the University of Gadjah Mada. The survey was coordinated by CIFOR (A.N. Gillison) with assistance from Ms N. Liswanti (CIFOR) and Dr D. Sheil (University of Oxford, Plant Sciences Department).

1.5 Collecting permits:

In accordance with existing Government regulations, prior arrangements were made via LIPI to permit staff from each of the international institutions to collect and curate taxonomic collections of plants and animals. In accordance with GoI regulations, all scientists who take collections overseas for identification are to return type specimens and a representative set of identified specimens to the respective partner institutions in Indonesia, in particular the Herbarium Bogoriense and the Zoology Museum. At the time of writing, all specimens have been returned together with their identifications by the overseas institutions.

1.6 Site location and description:

The survey site was located at Pasir Mayang in Jambi Province, Central Sumatra (Annex III; Maps 1,2,3,4). The area includes 900ha of a forest reserve set aside for research by SEAMEO BIOTROP located within the Barito Pacific logging concession. The survey team was based at the BIOTROP research station (with several members also located at the nearby Barito Pacific guest quarters). The area sampled is a mosaic of pristine forest, logged-over secondary forest, softwood plantations, rubber and jungle rubber with secondary mosaics of subsistence gardens and fruit orchards. While the forest is rich in plant species, the dominant tree genera are from

21 the Dipterocarpaceae family. Vegetation is supported by a mixture of relatively low nutrient, gibbsitic, kaolinitic and ferralitic soils over recent alluvium, acidic pumice tuffs, tuffaceous sandstones and carbonaceous mudstones siltstones and sandstones and conglomerates. The area is drained by the Batangahari river that is used to float log rafts down to Kota Jambi. Site locational and physical data including vegetation structure are listed in Annex III, Table 1a. Soil analytical data are contained in Annex III, Table 2.

1.7 Survey design:

1.7.1 General:

To forecast the effects of land-use on biodiversity at the landscape level requires an adequate sample of land-use intensity and land-use types. To set the bounds and system parameters in order to model ecosystem response to human impact requires a specific physical environmental context for land use. With this in mind, the present survey was preceded by a ground reconnaissance of a series of representative land use types (LUTs) in the lowland forested landscapes centred on Pasir Mayang in Jambi Province, Central Sumatra. Although only a limited number of LUTs could be sampled due to logistic constraints, these represented a range of extremes from pristine lowland tropical rain forest through logged-over forest and tree plantations to degraded Imperata grassland. Some specialist groups were restricted to only very limited samples (e.g. about 7 x 100m transects for termites alone) in the ten days available for fieldwork. It was therefore necessary to ensure these limited samples were effectively bracketed within a representative subset of vegetation and LUTs.

1.7.2 Gradient-based transects:

For surveys where the purpose is to recover as much information as possible about the distribution of plants and animals it is appropriate to use gradsect sampling (gradient-oriented transects) that rely on the purposive selection of sample sites arranged within a hierarchy of key environmental gradients (Gillison and Brewer, 1985) (Box 1). In the present case, these were rainfall seasonality, soil drainage patterns and time since harvest, or time since ‘opening’ (e.g. clearing rain forest). For this survey, LUTs were chosen primarily because of the nature of the land use and secondly according to environmental gradients in descending importance. At each LUT a pair of 40x5m strip transects was laid out along the contour where possible. The plot size was pre-determined from assessing results from range of plots elsewhere. As the results show, for most LUTs the 40x5m size is adequate. For very species-rich sites additional plots were added until the cumulative species curve reached a satisfactory (subjective) asymptote. The relatively small 40x5m plot makes it possible to sample animal habitat with a level of sensitivity frequently unobtainable with larger plots. Partly in preparation for this survey, CIFOR had produced a comprehensive digital elevation model (DEM) for Jambi Province compiled from 1:250,000 mapping scale topographic maps. These were supplemented by nested contour sub-maps compiled at 1:50,000 scale for focal survey areas surrounding the BIOTROP research site at Pasir Mayang (Annex III, Maps 1,2)

1.8 Database structure, storage and access:

Data from all collections of plants and animals were cross-referenced with the benchmark site numbers. All data are catalogued (Annex III, Table 15) and are stored on hard disk and as hard copy at CIFOR, as well as being backed up on 100mb Zip diskettes (IOMEGA). The data have been compiled in Microsoft Access and Excel formats. Field data were compiled on-site using

22 the newly developed CIFOR PFApro software. This software facilitates direct transfer of data to MSAccess. All data collated from the survey have been distributed to partner institutions, in line with ASB policy.

1.9 Data analyses:

The PATN exploratory data analysis package (Belbin, 1992) was used to detect patterns in the data sets by both classification and ordination (Multi-Dimensional Scaling), using Gower metric and Bray-Curtis measures. Linear correlations between all attribute values were calculated using the Minitab software package. Second order, polynomial regressions were also used to seek improved fits for those attributes with linear ‘r’ values >0.500 and where indicated by data distribution. These procedures helped identify the most efficient predictors of taxa and functional types and set the scene for further analyses using multiple regression.

Box 1 Gradient-based methods of survey design and data collection

The gradsect method of Gillison and Brewer (1985) employs purposively selected physical environmental gradients as a framework for survey. Sites are located along gradients according to a hierarchy of decreasing physical environmental influence and, usually, spatial scale (e.g. rainfall seasonality, temperature, parent rock type, slope, aspect, soil catena etc). This allows clusters of sites to be located to sample the maximum possible range of environmental variability that is responsible for species distribution and performance. Where the intent is to capture as much environmental variability and species distribution in the area, the method has been found more efficient than surveys based on purely random or purely systematic grid designs (see also Wessels et al., 1998). For plots (of 40 x 5m size) located along gradsects, a rapid survey proforma is used to record site physical variables (georeference by GPS; elevation (m), slope (%), aspect (deg.), soil type ( and subsequent physio-chemical analyses), parent rock type, and land-use history. Vegetation structure is recorded according to mean canopy height (m), percent crown cover, litter depth, furcation index, and basal area (m2 ha-1). All vascular plant species are recorded where possible (Family, Genus Species) and voucher specimens taken for subsequent taxonomic confirmation. Plant Functional Attributes (mainly features that indicate adaptations to environment) are recorded by in-country teams trained in the proforma method. The software package PFAPro developed by CIFOR to facilitate data entry and analysis was used to record data using a standard protocol to ensure compatibility and uniformity of data collection.

1.10 References:

Gillison, A.N. and Brewer, K.R.W. (1985). The use of gradient directed transects or gradsects in natural resource surveys. Journal of Environmental Management 20: 103-127

Wessels, K.J., Van Jaarsveld, A.S., Grimbeek, J.D. and Van der Linde, M.J. (1998). An evaluation of the gradsect biological survey method. Biodiversity and Conservation 7: 1093-1121.

SECTION 2: RAPID VEGETATION SURVEY

By A.N. Gillison

2.1 Introduction:

Evidence for the need to conserve biodiversity is well established in the literature and is reflected in the international Convention on Biological Diversity that has addressed a series of issues for attention by its signatories (CBD 1994). Despite the agreed urgency to develop a framework for biodiversity conservation, there is, as yet, no operational definition for biodiversity. According to Weitzman (1995), the implementation of any plan to preserve biodiversity is hampered by the lack of an operational framework or an objective function, and “We need a more-or-less consistent and useable measure of the value of biodiversity that can tell us how to trade off one form of diversity against another”. Miller and Lanou, (1995) also maintain “The value of biodiversity is determined largely by the interaction between human society and biodiversity”. This implies that among other things, there should be a dynamic link between biodiversity and productivity for human needs. The World Bank (1995) asserts it is necessary to integrate biodiversity concerns into national decision making, but the mechanisms for this remain elusive. In Indonesia, the Government recognises a lack of scientific and management expertise in biodiversity conservation (Government of Indonesia, 1993), that is further hampered by the current regime of property rights on public lands and waters, and the failure to use much of the financial returns from exploiting the country’s living resources to support biodiversity conservation (Barber et al. 1995).

These pressures highlight both the need for a working definition of biodiversity and a cost- efficient, generic tool for its assessment that can be used in turn to inform policy planners and managers. While the species remains the sole currency unit for biodiversity assessment (Heywood and Baste 1995) there will be little progress (cf. Wulff 1943). Species richness and abundance used alone and without other attributes of behaviour and performance can seriously misinform and impede biodiversity assessment. Parity in richness does not guarantee equivalence in either genetic composition or response to environment. Partly for this reason, an emerging school of thought now considers assessment should include functional features or types as well as species. (Box 1981, Gillison 1981, 1988, Nix and Gillison 1985, Cowling et al. 1994a,b, Huston, 1994, Collins, S.L. and Benning, T. 1996, Martinez 1996, Woodward et al. 1996). Varying definitions of functional types are so far most commonly associated with guilds (Bahr 1981, Gillison 1981, Huston 1994, Gitay and Noble 1996, Mooney 1996, Shugart 1996, Smith, 1996, Smith et al., 1996, Gillison and Carpenter 1997), but as Martinez (1996) asserts “..the functional aspects of biodiversity are a broad and vague concept that needs substantial added specification in order to become scientifically more useful.” Cramer (1996) also feels the task of screening all the world’s species for functional types is impossible and that for a global model, a breakdown of the world’s vegetation can only be done based on major physiognomic or otherwise recogniseable features. Recent global ecoregional studies (Gillison and Thomas, unpublished) suggest that, to the contrary, broad physiognomic and structural features can mask important functional and taxonomic differences in biodiversity. Gillison and Carpenter (1997) and Gillison (1997) and Gillison and Alegre (1999, unpubl.) have also shown it is possible to use generic functional or adaptive morphological attributes to characterise and quantify vegetation response to environmental change such as land use, climate and soil.

A new quantitative method has been developed for characterising vascular plants according to a set of 35 Plant Functional Attributes that describe a plant as a three component ‘coherent’ (sensu Vogel 1991) or functional model. This consists of the photosynthetic envelope, modified Raunkiaerean life form (Raunkiaer 1934) and above-ground root system. The method uses a semantic rule set and grammar (Gillison and Carpenter, 1997) to generate a theoretically finite set of unique PFA combinations for the world’s vascular plants. Any one combination is termed a functional modus. Using this rule set, about 7.2 million combinations or modi are possible, although it is thought that in reality the number is closer to 4,000. There is no a priori interdependence between modi and species; as the mapping is many-to-many, i.e. more than one modus can occur within a species and vice versa . The advantage of functional over solely species-based methods is that the former can be universally applied by observers with limited botanical and ecological experience. It can be used to compare functional characteristics of individuals and sets of individuals independently of species, e.g. where taxa may be geographically disjunct but may possess similar adaptations to environment. In a comparative study of methods of characterising site productivity and growth patterns in North Queensland rain forests, Vanclay et al. 1996 showed the PFA method outperformed traditional methods of site characterisation. The method is now undergoing further tests by the Forestry Department, Qld DPI (Keenan, Woldring pers. com.). Gillison et al., (1996) has shown consistently high correlations between total numbers of species and total numbers of unique modi recorded from 40 x 5m plots across a wide range of environments (Annex II and cf. Baskin 1994). The implications from this are that in surveys where botanical expertise is lacking, modi can be used to predict species richness with a high degree of confidence. This may also benefit rapid assessment of plant biodiversity and improve correlations between plant and animal biodiversity (cf. Gillison et al. 1996). A field proforma specifically designed for rapid survey (see section 2.4) can now be used by observers with minimal training to characterise site physical features, vegetation structure, species composition and modi to rapidly describe a specific habitat for a taxon or set of taxa.

Richness in species and unique modi can be a useful complementary descriptor of habitat. But while these contribute to characterising biodiversity, they do not by themselves reflect evenness or dominance of individuals per species such as the frequently used diversity indices of Shannon-Wiener and Simpson (Magurran 1988). Many diversity indices have been developed, but the search goes on (Cousins 1991, Majer and Beeston 1996). The great majority are based on species abundance and at best are usually regarded simply as another species-based, stand attribute. A problem for survey in tropical forests is that to generate such indices requires time- consuming counts of individuals which is not cost-effective. To help circumvent this problem, Gillison et al. (Appendix 2.1) have developed a complementary measure of functional diversity based on the numbers of modi per species for each plot. This differs from approaches by others (e.g. Martinez 1996) and has the advantage that in rapid survey it is only species rather than numbers of individuals of species that are counted. A measure of functional complexity has also been developed by the same authors based on a computed functional ‘distance’ between modal assemblages derived from a table of weighted ‘transformation’ values between specific PFAs (Gillison and Carpenter 1997).

It is one of the tenets of RBA that for practical purposes there should be indicators or surrogates of more complex plant and animal assemblages. Whether this is a realistic assertion is a continuing source of debate (Cranston and Hillman 1992, Reid et al. 1993, Pearson 1995, Howard et al. 1996), and there is often questionable theoretical support for targeting so-called keystone species (Tanner et al. 1994). There is nonetheless an increasing need for reduced attribute sets that can be used to carry other information such as the status of key pollinators

26 and seed dispersers that may not be available at the time of survey (Miller et al. 1995) To demonstrate indicator efficiency requires calibration from very intensive baseline studies of taxa and functional types at a comprehensive range of spatial, temporal and environmental scales. Such baseline studies are almost non-existent in complex tropical environments. Ongoing studies within the context of ASB show varying correlative trends. In a baseline study of Sumatran rain forests, Gillison et al. (1996) showed that while plant biodiversity increased with elevation from 500 to 900m asl, the converse was true for insects and birds. While such confounding effects can be accommodated by appropriate regression models and multiple discriminant formulations, predictive models of biodiversity based on environmental correlates such as elevation clearly need to be carefully evaluated before being used by managers. It follows that environmental context and scale are important in designing field studies of biodiversity (see also He, et al., 1994,).

Most practitioners now concede the landscape matrix is critical to supporting biodiversity (cf. Forman and Godron, 1986, Franklin 1993), and this has been central to survey design and data collection across all the ASB and CIFOR ercoregional benchmark sites. Because disturbance is a critical determinant of biodiversity (Petraitis et al.., 1989, van der Maarel 1993, Phillips et al.. 1994), factors such as agriculture, shifting cultivation and forest fragmentation (Grime 1979, Bierregard et al., 1992, Sayer and Wegge 1992, Margules and Gaston 1994, Brooker and Margules 1996) should be considered when designing a survey. For this reason, the ASB sites have been located specifically to sample a range of dynamic conditions, along successional gradients of land use from pristine rain forest, logged-over forest, plantations to degraded grasslands. Although the issue of plot size is a continuing source of debate in plant ecology, recent studies show that for plant diversity, useful information can be recorded from plots as small as 50 x 2m (Parker and Bailey 1992, Parker and Carr 1992, Parker et al. 1993) and 40 x 5m. (Gillison et al. 1996). The advantage of ‘small and many’ vs. ‘few and large’ is that the former is more cost-effective when sampling variation in biodiversity at landscape level (cf. Keel et al. 1992). Variation of this kind demands cost-effective survey techniques (cf. Margules and Haila 1996). Because the distribution of plants and animals is determined mainly by environmental gradients, gradient-based techniques using the gradsect approach offer one means of sampling such variation (Gillison and Brewer 1985). With gradsects, sites are located according to a hierarchical nesting of assumed physical environmental determinants such as climate, elevation, parent rock type, soil, vegetation type and land use. This approach has been shown to be more cost-efficient than purely random or purely systematic (e.g. grid-based) survey design (Gillison and Brewer 1985, Austin and Heyigers 1989). As gradients themselves are being sampled, this will enhance the efficiency of extrapolative spatial models.

Issues of biodiversity conservation inevitably raise important questions of site representativeness. For a programme involved in the selection of ‘best-bet’ options for biodiversity and productivity, a manager may need to choose between different locations to ensure optimal management. For this a range of sophisticated computer-based solutions already exists. These are based mostly on species occurrence but may include environmental features such as land classes (Nicholls and Margules 1993, Pressey et al. 1996, Csuti et al.. 1997, Pressey et al. 1997). Other species-based approaches use additional levels of higher taxa (Prance 1995) or a measure of ‘phylogenetic distance’ to include taxic richness or genealogical relationships as embodied in taxonomic classifications, typically by a weighting of the relative number of species per genus, genera per family etc. (Vane-Wright et al. 1991, Williams et al.. 1992, Faith 1992, 1993, 1995). A problem with species-dependent approaches of this kind is that for many tropical lowland forests, species identification is difficult and time-consuming. In addition, the majority of these algorithms require expertise that is frequently lacking in

27 developing countries. For this reason, and because functional types can be more easily identified than species, Gillison et al., (unpublished 1998) developed an analagous concept of ‘functional distance’ based on modi (outlined in Annex I). The algorithm is being incorporated in a new data-entry software package PFAPRO designed to run on a PC as a Windows application (Carpenter and Gillison, unpublished 1998). When data from a series of plots containing functional modi have been entered, PFAPRO has the facility to generate a distance matrix on demand. By this method, managers can readily identify levels of similarity between plots or landscape units.

Data collected during this project will be used to generate and test spatial models of key sets of taxa and functional types and to couple these with productivity patterns based on land use. For this purpose a potential mapping software package DOMAIN (Carpenter et al. 1993) will be used. Unlike other packages such as BIOCLIM (Busby 1991) or CLIMEX (Sutherst and Maywald 1985) that are either climate-dependent or require detailed, process-based knowledge about the species in question, DOMAIN uses any georeferenced data that are considered important in influencing performance of an individual. This may include environmental data used to construct a gradsect–based survey. DOMAIN then accepts known distribution points for specific taxa or functional types and constructs an environmental envelope for these using environmental correlates and a distance measure based on the Gower metric. It then computes a grid-based distribution map of according to the similarity matching of each pixel or grid with the original environmental domain. DOMAIN has been used in previous baseline studies in Sumatra (Gillison et al. 1996) and has been modified by CIFOR to run as a user-friendly, Windows based package on a PC. The software is available gratis from the CIFOR home page on the internet. Since its installation in August 1997 CIFOR has recorded downloads from users in 35 countries. Because DEMs were constructed for each of the ASB benchmark sites in Phase II, it is anticipated DOMAIN will be used for generating and testing spatial models of biodiversity and related productivity. The effective extrapolation of data will depend to a large degree on the availability of georeferenced environmental data. These data have been compiled at CIFOR using mapping sources from within Indonesia (Laumonier et al. and other sources from within the GoI Ministry of Forestry). Remote sensing of tropical rain forest vegetation has been used elsewhere with some success (Tuomisto et al. 1994) and is expected to play a significant role in DOMAIN applications. Data for normalized difference vegetation index (NDVI) are available and can be used in DOMAIN. NDVI is commonly used with AVHRR (advanced very high resolution radiometer) data for which appropriate calibrations are necessary (Roderick et al., 1996a,b).

Most vegetation classification and survey methods incorporate a combination of broad structural variables coupled with seasonality (deciduousness) and a list of dominant species, e.g.’Very tall evergreen Dipterocarp forest’. While this is useful for many geographic purposes it is insufficiently diagnostic for management purposes. In addition, structurally similar vegetation types are usually annotated by regionally different plant species. Within a region, vegetation described according to vegetation structure may be adequate for describing animal habitat but similar structure in separate global ecoregions are not necessarily ecologically equivalent. For ecologically sensitive classifications additional, response-based attributes such as adaptive features or plant functional attributes (PFAs) provide added value. As PFAs are generic and largely independent of species, they can be used to make ecological comparisons between geographically remote areas where environments and adaptive features may be similar but where species differ.

2.2 Methods:

The Plant Functional Attribute proforma (modified from Gillison, 1988 and updated by Gillison and Carpenter, 1997) was used to record site physical features [georeference by GPS in degrees, minutes and seconds; slope percent (clinometer); elevation (m) (digital aneroid altimeter); aspect in degrees (compass); parent rock type; soil type; vegetation structure, (mean canopy height (m), crown cover percent, basal area (m2ha-1); litter depth (cm); Domin scale cover-abundance estimates of wood plants <2m tall and Domin estimates of bryophytes; all vascular plant species and plant functional types (PFTs]. As described by Gillison and Carpenter (1997), Plant Functional Types or PFTs or functional modi are combinations of essentially adaptive morphological or functional attributes (e.g. leaf size class, leaf inclination class, leaf form and type (distribution of chlorophyll tissue), coupled with a modified Raunkiaerean life form and type of above-ground rooting system. PFTs are derived according to a specific grammar or rule set from a minimum set of 35 functional attributes. An individual with microphyll-sized, vertically inclined, dorsiventral leaves supported by a phanerophyte life form would be a PFT expressed as MI-VE-DO-PH. Although they tend to be indicative of a species, they are independent of species in that more than one species can occur in one PFT and more than one PFT in a species. PFTs allow the recording of genetically determined, adaptive responses of plant individuals that can reveal infraspecific as well as interspecific response to environment (e.g. LUTs) in a way that is not usually contained in a species name. They have a major advantage in that, because they are generic, they can be used to record and compare data sets derived from geographically remote regions where, for example, adaptive responses and environments may be similar but where species differ. The data are recorded along a 40x5m strip transect located along the contour.

The data were compiled in a laptop computer using a recently developed software package, PFAPro (Gillison and Carpenter, unpublished). PFAPro facilitates compilation according to the rule set developed by Gillison and Carpenter (1997). It also facilitates the summary analysis of meta-data as well as producing graphs of relationships between different plant and vegetation variables. Using PFAPro, data logged for each 5x5m quadrat allow the generation of cumulative species and PFT totals per unit area and this allows the subjective inspection of asymptotic curves that can indicate whether or not a plot is an adequate sample of the vegetation or LUT (See Annex 1, Fig.1).

In addition to site physical data, simple totals of species, PFTs and vegetation structural variables, PFAPro can be used to generate a range of diversity indices for PFTs (Shannon- Weiner, Simpson and Fisher’s alpha). The calculations are not trivial as, unlike diversity indices for species that are based on abundances of individuals per species, the PFT indices are derived on the number of species per PFT. Since the species to PFT relationship is many-to- many, this must be taken into account when calculating diversity. The method is described more fully in Appendix 2.1.

Four observers (ecologist and assistant, botanist (x2) and two laborers) collected plant voucher material later identified and curated at the Herbarium Bogoriense. A complete set of identified species and associated PFTs is listed in Annex III, Table 3. This method facilitated sampling even the most complex rain forest plot of 177 species in less than three hours. Photographic records were made of each plot. A sub-set of these has been scanned and will be cross- referenced with the data set.

2.2 Results:

The data were analysed according to the methods described above and in Part B. The most useful interpretations came from multidimensional scaling in which a two vector solution was extracted from plot data (Part B, Annex I, Fig. 7). This graph shows a zone of maximum biodiversity richness that is associated with jungle rubber. The peak in richness can be explained in part by the greater variety of available ecological niches in this agroforestry system compared with pristine rainforest. The analyses are based on a minimum data set of mean canopy height, basal area, species richness, PFT richness and a ratio of species numbers to numbers of PFTs or modi. Cumulative species, modi and species/modi richness area curves per 40x5m plot are indicative of vegetation type per LUT (Part B, Annex I, Fig.1 (1-7)). More detailed results from analyses of combined sets of taxa and functional types are described in the synthesis (Section 11). Other analyses dealing with variations on compositional structure of species, PFTs and vegetation structure and their relation to LUT will be dealt with in a later report. Plant taxa and functional types for each LUT are listed in Annex III Table 3. Summary data are listed in Table 2.1 and estimates of green biomass are given in Table 2.2 below. Relationships between vegetation and LUTs are described briefly in Section 3 below.

Table 2.1. Summary of Taxa and Plant Functional Types (Modi) per LUT

No. Site Family Genus Species Uniq Sp/Plot Modi 1 BS1 44 82 103 102 37 2 BS2 43 81 104 100 36 3 BS3 32 48 50 50 20 4 BS4 45 83 111 108 39 5 BS5 43 82 117 112 38 6 BS6 26 35 42 42 27 7 BS7 25 43 48 46 33 8 BS8 37 60 68 65 37 9 BS9 31 52 58 54 30 10 BS10 53 97 115 111 47 11 BS11 49 89 100 97 41 12 BS12 6 10 11 11 10 13 BS13 6 7 7 7 5 14 BS14 7 12 15 15 12 15 BS15 8 19 19 19 13 16 BS16 22 40 43 42 34 Total 477 840 1011 981 459

Unique Total 91 320 _ 557 216

Table 2.2. Green biomass per Land Use Type*

Site.no LUT Av.kg/m2 stdev coefvar SEM C-t/ha BS01 NF 0.133 0.079 0.594 0.028 0.533 BS02 NF 0.000 0.000 * 0.000 0.000 BS03 LOF 0.000 0.000 * 0.000 0.000 BS04 LOF 0.045 0.083 1.854 0.029 0.179 BS05 LOF 0.007 0.020 2.828 0.007 0.028 BS06 HTI 0.247 0.159 0.642 0.056 0.987 BS07 HTI 0.092 0.131 1.424 0.046 0.368 BS08 RUB-P 0.107 0.126 1.178 0.044 0.426 BS09 RUB-P 0.083 0.093 1.121 0.032 0.331 BS10 J_RUB 0.033 0.400 1.194 0.014 0.133 BS11 J_RUB 0.018 0.035 1.913 0.012 0.073 BS12 IMP 0.227 0.057 0.252 0.033 0.908 BS13 IMP 0.180 0.008 0.045 0.004 0.719 BS14 CAS 0.207 0.028 0.136 0.016 0.829 BS15 CAS 0.288 0.089 0.308 0.051 1.150 BS16 CHROM 0.335 0.143 0.427 0.082 1.340

*Source M.Van Noordwijk and K.Hairiah

NOTE: Additional results from the vegetation survey are described in Section 11 2.3 References:

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Appendix 2.I

Unpublished measures of functional diversity and functional complexity used in this project

(extracted from Gillison, A.N. Carpenter, G. and Thomas, M., Plant functional diversity and complexity: two complementary measures of species diversity.)

Functional diversity

Concepts of functional diversity vary; according to Martinez (1997) (see also Steele, 1991 quoted by Martinez), functional diversity is defined as “..the variety of interactions with ecological processes” and can be quantified by determining the nature and extent to which functional groups are represented in an ecological system. Functional diversity can also refer to the number of such groups in a community each of which contains one or more species (Smith and Huston, 1989; Scott and Benning 1996). Whatever the nature of the functional groups it is generally accepted they will be fewer than the species under study, (Mooney 1997). In this sense functional ‘diversity’ is simply a measure of group richness rather than an estimate of evenness or dominance based on the abundance of individuals per group.

As with species diversity, it would seem reasonable to derive a parallel measure of functional diversity based on the abundance of individuals per functional type or modus but without species-weighting. While logically viable, this is likely to be limiting in practice as to record all individuals, (e.g. in an epiphyte-rich, rain forest) can be excessively time-consuming and counterproductive if the aim is rapid assessment, and if the functional types or groups are likely to be significantly fewer than the species. Depending on the scale an purpose of the investigation, the additional effort may not be worth the gain. For these reasons, we explore the possibility of using species instead of individuals to serve as a ‘higher-order’ measure of abundance by deriving a species-weighted, rather than a spatial or density-driven, measure of Functional Diversity based on abundances of individuals. A species-weighted form of Functional Diversity (SFD) can therefore be defined as: The diversity of functional types expressed as a function of the number of species per type. While the definition can be compared with that of Huston (1994) for species diversity where “The total species diversity of a community is described by the number of functional types multiplied by the average number of species per functional type”, this approach is more sensitive to evenness and dominance. We achieve this in the same way that species abundance is used to calculate species diversity but with the important difference that counts of species per functional type are used instead of counts of individuals per species. For this we apply the Shannon-Wiener formula to estimate evenness and that of Simpson to estimate dominance. Another difference is that, unlike the ‘one-to-many’ species to individual relationship, the mapping between species and modi is’

35 many-to-many’ (i.e. more than one species can exist in one modus and vice versa) (Fig. 1). Both formulae have been modified to accommodate these multiple relationships.

Species Individual Modus

1 A m1

3 B m2

Fig. 1 An example of multiple linkages (many-to-many mapping) between Linnean species and functional types or modi. Species A occurs in modi m1, m2; species B in modus m2, while m1 occurs in species A , and m2 in species A and B. An individual is recorded once if it satisfies any one of these relationships – duplicates are omitted. Shannon Wiener Index

The Shannon-Wiener index is calculated from the equation (ref.):

Nspp Hpp'ln=−∑ ii j=1 where quantity pi is the proportion of individuals found in the ith species, and is estimated using the maximum likelihood estimator:

n p = i i N

Where ni is the number of individuals in the ith species. For species/population data, each individual in the sample belongs to exactly one species. And N is the total number of species recorded. However with modus/species data, a species may be attributed to more than one modus if that species is present in multiple functional forms. To accommodate this difference, the maximum likelihood estimator is modified to divide the proportional count for a species evenly between the modal types in which that species is present. The equation for pi, the proportion of species in the ith modus becomes:

Nspp n p = ji i ∑ nN. j=1 j

Where Nspp is the number of species, nji is the number of records for species j, modus i (either 0 or 1) nj is the number of records for species j, and N is the total number of records.

Because the species to modus mapping is a many to many relationship N may be greater than both the number of species Nspp and the number of modi in the sample.

Simpson’s Index

The same modified form of the maximum likelihood estimator is used in the calculation of the Simpson index which is usually formulated as:

2 Dp= ∑ i

The Simpson index produces higher values for lower diversity, and is often expressed as 1− D .

Limits

Diversity values for the Shannon-Wiener index become progressively smaller with increasingly uneven distribution of species between modi where, for example, a small number of modal forms dominate the sample. Given the number of species and the number of modi in the sample, the absolute minimum index value possible can be found by computing the largest possible value for maximum likelihood estimator (P0) for one modus, while minimizing the remaining Nm-1 estimators (Pi>0). The minimum estimator value occurs when only one species occurs in a modus, and that same species occurs in all other modi. The minimum is formulated as:

Nm − 1 p =−1 0 Nm. Nspp 1 pi>0 = Nm. Nspp

HppNmpp'ln()lnmin =−[]00() + −1ii>> 0 ( 0 )

The maximum value of the Shannon-Wiener index is generated when the species are evenly distributed between all modi, such that Pi =1/Nm, yielding as a final form:

HNm'lnmax = ()

The same proportion values determine the limits of the Simpson Index. This index returns smaller values for increasing diversity.

1 D = min Nm

2 2 DpNmpmax =+0 () −1i>0 Interpretation and Examples

When interpreting species-weighted functional diversity measures it is important recall that the measure describes the distribution of species between functional modi, not the distribution of individuals between functional types.

The values generated by of these species-weighted functional diversity measures, when applied over a broad range of sites, are typically higher than the equivalent measures from species/population data. This reflects the reduced likelihood of dominance of any particular functional type, and a similar degree of discriminatory resolution (or granularity) between functional types and species. The consistently high correlation between species counts and modal counts at the global level is explored elsewhere (Gillison, submitted for publication – see Annex II)

Functional complexity

Two approaches were adopted for the analysis of modal composition. The first was an exact mirror of the analysis of species composition. Instead of an analysis of species incidence, the incidence of each modus was used to generate a between-site Jaccard distance. This distance matrix was then input to the same multidimensional scaling procedure. The second approach attempted to take account of the inherent similarity or dissimilarity between different modi. It was based on the syntactic distance between modi of Gillison and Carpenter (1997).

We consider sites X and Y, such that site X contains the set of modi {Xii , = 1K m} and site Y contains the set of modi {}Yii , = 1K n. Now let fab( ,) be the distance between modi a and b. We define the dissimilarity between sites X and Y to be:

m n

∑minfXY()ij,,+ ∑ min fX( ji Y) j j d = i i . XY, mn+

This index will be zero only if sites X and Y contain the same set of modi. In particular, it will be non-zero if modi at one site are a proper subset of modi at the other. It should also be noted that the dissimilarity index is not a metric. The expected value of the dissimilarity index depends on the number of modi at each site. If the modi present at each site are generated by random sampling from a set of available modi, then the distance between two sites will decrease as the number of modi at each site increases. In the absence of any other aspect of pattern, we would expect sites with many modi to be very similar, whilst sites with few modi would be dissimilar - both to other sites with few modi and to sites with many modi. Ordination of such a dissimilarity matrix would result in a hyper-sphere - with modi rich sites at the centre, and modi poor sites at the periphery. Analyses of data from a range of global environments tend to confirm the utility of this procedure (Gillison and Thomas, unpublished).

SECTION 3: VEGETATION AND LAND USE TYPES

Suhardjono & J.J. Afriastini

Herbarium Bogoriense, Botanical Division, Research and Development Center for Biology LIPI, Jl. Juanda 22, Bogor , Indonesia.

3.1 Background:

The forested lowland in Sumatra is a highly complex ecosystem that is of great interest for research Compared with other natural ecosystems in Sumatra, perhaps the forest lowland ecosystem is the most comprehensively studied so far. In Sumatra biodiversity is highest in lowland vegetation. For instance, in the valley surrounding the Ranum river in North of Sumatra, for vegetation which has diameter 15 cm has Simpson Index diversity 0.96 and 0.94 on Bangka island hill (from result study by Tim PUSLIT SDL-USU). Some studies on the phytosociology of forest of the pamah land use type have been done in Sumatra (Mogea 1980; Mirmanto 1986; Abdulhadi et al. 1989, 1991; Abdulhadi 1991 and Mirmanto dkk. 1992). Nonetheless the structure and composition of vegetation vary from one to another place depending on habitat condition. Human activity in forestland use will greatly influence changes in vegetation composition.

3.2 Aims and objectives:

• To provide baseline data for above-ground biodiversity assessment based on vascular plant species, plant functional types, vegetation structure and key site physical attributes. • To provide a biophysical baseline and sample reference point for other multidisciplinary input. • To identify the best sub-set of plant-based variables that be used to estimate distribution in other biota.

3.3 Personnel:

Dr A.N. Gillison, Plant Ecologist (CIFOR) Ms N. Liswanti, Research Assistant (CIFOR) Drs Suhardjono, Botanist (LIPI, Herbarium Bogoriense) Mrs Afriastini, Botanist (LIPI, Herbarium Bogoriense) Mr Edi Purnomo, Botanist (BIOTROP)

3.4 Methods:

Based on radar satellite and remote sensing of lowland forest in Jambi, there were 8 land use types in Pasir Mayang, Pancuran Gading village, and Kuamang Kuning. They are primary forest, secondary forest, selectively -logged forest 1983, Paraserianthes plantation 1993-1994, rubber plantation, jungle rubber, Chromolaena fallow, Imperata and Cassava garden. For each land use type we recorded the spatial coordinate using GPS, elevation, slope, soil depth, soil type, canopy height, using a 40x5 m strip transect. In each plot we recorded all the vascular plant species, life form, leaf size, leaf inclination, and herbarium specimen for identification at Herbarium Bogoriense, Balitbang Botany, Puslitbang Biology-LIPI, Bogor.

3.5 Discussion:

The results of herbarium identifications from the survey are 765 species. There are 83 families, 276 genera and 428 species (Annex III, Table 2). Selective logged forest (1983) contained the highest species recorded during the survey, followed by jungle rubber, intact rain forest, rubber plantation, secondary rain forest, Paraserianthes plantation, Chromolaena fallow, Cassava plantation, and Imperata grassland. The diversity of vegetation on this study is higher than land use forest in Riau. In other studies of the pamah land use type in Bukit Tigapuluh, Riau, Sumatra on secondary forest (belukar) within 0.25ha sample plots, Mirmanto (1993) recorded 19 families, 30 genera and 45 species. In disturbed forest within 0.25ha sample plot, there were 27 families, 48 genera and 57 species. In primary forest, there were 20 families, 37 genera and 60 species (Annex III. Table 3).

3.6 References:

Abdulhadi, R. E. Mirmanto and Yusuf,R. (1989). Struktur dan komposisi petak hutan Dipterocarpaceae di Ketambe, Taman Nasional G. Leuser, Aceh. Ekologi Indonesia 1(2):29-36. Abdulhadi, R., R. Yusuf and Kartawinata, K. (1991). A riverine tropical rain forest in Ketambe, G. Leuser National Park, Sumatera, Indonesia. Biotrop Spec. Publ. 41:247- 255. Abdulhadi, R. (1991). A Meliaceae forest in Ketambe, G. Leuser National Park, Sumatra, Indonesia with special reference to the status of Dipterocarps species. Biotrop Spec. Publ. 41: 307-315. MacKinnon, J.R. (1974). The behaviour and ecology of wild orang utan (Pongo pygmaceus). Anim. Behav. 22:3-74. Mirmanto, E. (1994) Fitososiologi hutan lahan pamah di kawasan Bukit Tigapuluh, Riau, Sumatra. Dalam : Sanbukt, O. and H. Wiridinata (ed.) Rain Forest and Resource Management. Proceeding of the NORINDRA Seminar, Jakarta 25-26 May 1993, LIPI : 29-35 Mogea, J.P. (1980). Komposisi flora pohon hutan primer di Biak Mentelang, Kutacane, Aceh Tenggara. Dalam : Budiman, A. dan K. Kartawinata (eds.) Laporan Teknik 1979-1980. Bogor, LBN-LIPI :137-139.

SECTION 4: BIRDS

By P. Jepson1 and Djarwadi2 1School of Geography, Mansfield Rd, Oxford, UK, 2Forestry Faculty of Bogor Agricultural Institute,Bogor

4.1. Introduction:

This report presents preliminary analyses and conclusions of the bird survey component of the Jambi base-line study. The analysis is largely descriptive and aims to provide an overview of the data to facilitate comparisons with findings from other disciplines and generate ideas for more detailed and multidisciplinary analysis. The conclusions section flags some areas which may merit further investigation. A brief discussion on the sampling protocol suggests that for birds, an approach drawing on a landscape ecological framework may be more suitable for Rapid Biodiversity Assessment that aims to assess the impact of land use change.

4.2. Aims and objectives:

• To provide baseline data for above-ground biodiversity assessment based on bird species richness and functional (guild) type.

• To investigate the changes in bird diversity across a disturbance gradient from natural forest to agricultural habitats.

• To provide a sample reference point for other multi-disciplinary input.

4.3. Personnel:

Paul Jepson, Ornithologist (University of Oxford) Djawardi, Ornithologist (IPB, Bogor)

4.4. Methods:

4.4.1 Review of existing methods:

For the purpose of measuring and comparing bird diversity there are two broad groups of methods: those which generate a species list, perhaps with an approximation of abundance, and those which generate a species list with a quantifiable measure of abundance.

For birds, abundance is enormously difficult to measure with any precision. A key problem is the difference between observed and real abundance. This can be a factor of a species’ habits and the openness of a habitat (distance at which birds can be seen and/or heard). The latter variable differs between habitat types and must be accounted for if the aim (such as in this study) is to compare diversity between habitats. A group of methods called Distance Sampling [Reynolds, 1980] which are supported by a sophisticated analytical statistics package (DISTANCE2) are available for comparing abundance in different habitats. One of these methods (Variable-circular Plot) has been employed by the BirdLife International-Indonesian Programme in Nusa Tenggara and Maluku to compare biodiversity values of different habitat

41 types with proposed reserves. Although distance sampling is highly compatible with a plot- based protocol, it was not considered appropriate for the survey because BirdLife’s experienced has revealed that:

• while a density can be calculated with five contacts for a species, twenty contacts are usually required to generate densities within 5% confidence limits; this requires planning for at least 8 days sampling for each habitat type; • data analysis is complicated and time consuming; • it is questionable if the assumptions of Distance Sampling methodology are justified in tropical rainforest.

“Rapid” as defined by the time horizon of this study, constrained the choice to presence- absence methodologies and those which could yield useable data in one day's sampling per division. Species accumulation curves were selected. This method is well known in Indonesia because it was described by John MacKinnon in his popular field guides. Counts of species are made during successive sampling units, and the cumulative number of species plotted. The rate at which the curve flattens gives an indication of total number of species and whether all species in the habitat have been observed.

MacKinnon defined the sampling unit as the first 20 species and envisaged an observer walking. This introduces a rough measure of relative abundance and increases the likelihood of meeting rare species. The need to link bird observations to a plot, as well as time constraints, required a variation of this methodology - observers stayed with the immediate vicinity of the plot and the sampling unit was a five minute time period. With this protocol an abundance measure is not possible, and rare species are likely to go unrecorded.

4.4.2 Field Methods used on this survey:

Twelve plots were sampled using a species accumulation methodology. A species list of contacts was compiled for each five minute period between 16.30hrs and 18.00hrs and between 06.30 hrs. and 08.00 hrs. Audio-visual species contacts were made by the two observers named above in 'wooded' land use types and by a single observer in 'open' land use types. The observers roved within 30m of the plot centre.

Bird species contacts were scored: “H” = heard, “L” = seen, “T” = fly-over. In open habitats a list was made of species actually recorded in the land use.

Data were entered into a spread-sheet after each morning count. Entering data while a count is within immediate memory is an integral part of the overall methodology, because it: a) assisted with learning\confirming identity of calls; b) ensured both observers gave the same name to the same contact.

In additional to the above, bird species lists were complied for three landscape elements of the logged forest land-use not sampled in the plots, namely : access road edge; camp; and log pond.

4.4.3 Analysis:

4.4.3.1 Data storage and access:

42 The following two data sets are annexed to this report and contained in the Excel file name 'Jambird.xls': 1. A matrix of species recorded in each 5-min count. 2. A total species list by plot and also by additional “landscape elements”. (Annex III, Table 4)

Data set 1 is a combined and agreed record taken from the field notebooks of the two observers. The second data set is compiled from the first. Additional values attributed to each species in order to facilitate investigation of the data sets are as follows:

• Species number code according to [Andrew, 1992 p.147]; • Species number code specific to this study. i.e. the number of the species in the total species list for the study ordered according to [Andrew, 1992 p.147]; • Status i.e. resident ( R ), migrant (M); • Diet guild sensu [Thiollay, 1995 p.199] • Feeding site guild sensu [Thiollay, 1995 p.199] • Body size category sensu [Thiollay, 1995 p.199]

4.4.3.2 Data analysis:

The methods used resulted in a presence-absence data set. Although species were recorded by five minute count, it is not possible to analyse for relative abundance because counts are not independent, i.e. a bird recorded in one count may or may not be the same bird recorded in subsequent counts.

Three species flying over the plot and unlikely to utilise the LUT in which the plot is embedded, were omitted from the analysis (see Appendix 4.1 for list).

To explore the question of the impact of disturbance on forest bird diversity the following analyses were made:

a) Species richness. Species accumulation curves were plotted to compare species richness between plots. The intention is to re-analyse this data using the British Museum program “Curves” which optimise the curve. This analysis will be submitted as an update to this report.

b) Functional diversity. Species were assigned to diet guilds, foraging site guilds and body size classes sensu [Thiollay, 1995 p.199]. Counts of number of species per class are graphed. Unidentified species were omitted from the analysis. A table of number of species according to taxonomic family is also presented.

c) Resident\migrant status. A simple count of migrant species by plot was made to ascertain whether numbers differed between plots.

d) Differentiation in β diversity between sites. Sørenson’s similarity indices were calculated using the Multivariate Statistical Package (MVSP), 1987. This is a simple measure suitable for presence and absence data; it treats all species as equal irrespective of whether they are abundant or rare. [Magurran, 1988 p. 200]

43 e) Clustering of sites. A nearest neighbour cluster analysis was performed on the Sørenson’s similarity indices with randomised data input.

4.5. Preliminary results:

4.5.1. Descriptive analysis:

4.5.1.1 Species richness:

60 Plot BS01 Plot BS02 Plot BS03 50 Plot BS04 Plot BS05 Plot BS06 40 Plot BS07 Plot BS08 Plot BS10 30 Plot BS12 Plot BS13 Plot BS16 Cumulative total 20

0 2 4 6 8 1 2 4 6 8 P P P P P 10 12 14 16 P A A A A 10 12 14 16 18 P P P P A A A A A 5 min counts

Figure 4.1 Species accumulation curves

Figure 4.1 shows species accumulation curves for each plot. The rate at which the curve flattens is crucial to comparing such curves, and it is regretted that it is not possible as yet to present smoothest-fit curves.

The richest plots were the natural forest plots (BS2 & BS5) and the heavily disturbed logged forest plot (BS3). The two industrial plantation plots (BS7 & BS8) were the most depauperate. The 'wooded' plots appear to show increasingly depauperate sub-sets with increased intensity of management. The 'non-wooded' plots have a largely open-country species assemblage distinct from the 'forest' plots. The Chromolaena plot (BS16) has some forest species and represents the change-over point.

44 4.5.2.1 Trophic diversity:

4.5.2.1.a Proportion of diet guilds:

Figure 4.5.2.1i. Percentage of species diet guilds

100%

90%

80% Omnivore 70% Granivores 60% Frugivores 50% Nectarivores Insectivores 40% Carnivores 30%

20%

10%

0% BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

Figure 4.5.2.1b. Number of species diet guilds

Omnivore 40 Granivores Frugivores 30 Nectarivores Insectivores 20 Carnivores

0 BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

The gramnivore guild is represented in disturbed forest habitats and constitutes the largest percentage of species richness in the most highly modified habitats: Imperata; Cassava and Chromolaena (plots BS12, BS13 and BS16). Plantation rubber (BS8) and jungle rubber (BS10) have similar proportions of each guild, as do the three logged forests. The two Paraserianthes plots are dissimilar - BS6 has gramnivores and no nectarivores, whereas for BS7 it is the reverse (species numbers are low).

45 4.5.1.2b Proportion of feeding site guilds:

Figure 4.5.1.2i. Percentage of species in each feedings site diet guilds 100%

90%

80% 70% Shrubs 60% Ground 50% Tree structure 40% Understory 30% Tree canopy 20% Aerial

10%

0% BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

Figure 4.5.2.2a. Number of species in each feedings site diet guilds 60

40 Shrubs Ground 30 Tree structure Understory 20 Tree canopy Aerial 10

0 BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

The tree canopy feeding guild constitutes over 45% of species in natural forest plots (BS1-BS5) and in the jungle rubber (BS10) and plantation rubber (BS8). The proportion of this guild is less than 35% of species total in all other plots. Species adapted to feeding from grasses and shrubs are present in all non-natural forest plots, but not in natural forest plots (with the exception of one species in Plot BS5).

46 4.5.2.3 Proportion of body size classes:

Figure 4.5.2.3 a. Percentage of species by size class 100% 90% 80% 70% 60% 50% 1281-2560g 40% 641-1280g 30% 321-640g 161-320g 20% 81-160g 41-80g 10% 21-40g 0% 11-20g <10g BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

Figure 4.5.2.3 b. No of species by size class 60

30 1281-2560g 641-1280g 20 321-640g 161-320g 81-160g 10 41-80g 21-40g 0 11-20g <10g BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16

No clear patterns differentiate the plots. Both the unlogged natural forest site (BS1) and Imperata (BS12) have the largest percentage of species in the heaviest weight class.

4.5.1.4 Species diversity by taxonomic family:

Natural forest plots are characterised by higher numbers of bird families and passerine bird families, compared with non-natural forest families (with the exception of BS1), see Table 4.1. Five families are only present at natural forest plots, namely: Hemiprocnidae; Trogonidae; Muscicapidae; Monarchidae; Zosteropidae.

47 Table 4.1 Summary of species in each taxonomic family by plot

Natural Forest Plots

BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16 Ardeidae 1 Accipitridae 1 1 1 1 Falconidae 1 1 Anatidae 1 Phasianidae 1 1 Turnicidae 1 1 1 1 Rallidae 1 Charadriidae Scolopacidae Glareolidae Columbidae 1 1 1 1 1 2 3 2 3 Psittacidae 2 2 2 2 2 2 2 1 1 Cuculidae 1 2 3 2 3 1 4 2 2 4 Strigidae Caprimulgidae Apodidae 1 1 3 Hemiprocnidae Hemiprocnidae 1 1 1 1 Trogonidae 1 1 1 1 Alcedinidae 1 1 1 1 1 1 1 Meropidae 1 1 2 1 1 1 1 1 1 1 Capitonidae 1 1 2 2 1 2 1 Picidae 3 1 3 3 1 1 2 1 Eurylaimidae 2 1 1 1 1 1 Hirundinidae 1 1 1 1 1 1 Campephagidae 1 2 2 1 Pycnonotidae 2 2 3 4 3 3 2 3 4 2 2 2 Irenidae 1 2 2 1 2 1 1 Laniidae 1 Orthonychidae 1 Timaliidae 4 6 3 3 6 1 2 1 3 1 1 3 Sylviidae 1 3 3 3 3 5 4 2 3 6 5 5 Muscicapidae 2 1 1 1 2 Monarchidae 2 1 1 4 Dicaeidae 1 1 2 2 2 1 2 1 Nectariniidae 3 1 3 3 6 2 1 1 1 Zosteropidae 1 1 Estrildidae 1 2 2 2 Ploceidae Sturnidae 1 2 2 1 2 1 1 1 1 1 Oriolidae 1 1 1 Dicruridae 1 1 2 1 2 1 1 1 Corvidae 1 1 1 2 1 non-passeriformes 6 8 7 9 10 6 5 7 5 8 10 5 passeriformes 10 13 13 12 15 8 9 8 9 5 6 11 Total Families 16 21 20 21 25 14 14 15 14 13 16 16 .

48 4.5.1.5 Migrant species:

Figure 4.5.1.4i Number of migrant bird species by plot and landscape element

Number of species

0 BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS6 Road LPond Camp

Migrant species are more frequent in non-natural forest plots and in disturbed landscape elements within the natural forest.

4.5.2 Exploratory (pattern) analysis

4.5.2.1 Differentiation in β diversity (Sørenson similarity indices=Dice’s Coefficient)

Table 4.2. Similarity indices

BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS10 BS12 BS13 BS16 BS1 1 0.535 0.389 0.423 0.381 0.107 0.143 0.080 0.159 0.036 0.036 0.164 BS2 0.535 1 0.482 0.390 0.442 0.269 0.239 0.197 0.162 0.091 0.121 0.167 BS3 0.389 0.482 1 0.578 0.563 0.235 0.206 0.258 0.320 0.060 0.090 0.219 BS4 0.423 0.390 0.578 1 0.568 0.299 0.299 0.295 0.486 0.121 0.121 0.306 BS5 0.381 0.442 0.563 0.568 1 0.225 0.275 0.270 0.299 0.101 0.127 0.235 BS6 0.107 0.269 0.235 0.299 0.225 1 0.615 0.391 0.271 0.510 0.549 0.491 BS7 0.143 0.239 0.206 0.299 0.275 0.615 1 0.435 0.407 0.431 0.471 0.561 BS8 0.080 0.197 0.258 0.295 0.270 0.391 0.435 1 0.453 0.222 0.267 0.275 BS10 0.159 0.162 0.320 0.486 0.299 0.271 0.407 0.453 1 0.172 0.172 0.375 BS12 0.036 0.091 0.060 0.121 0.101 0.510 0.431 0.222 0.172 1 0.840 0.571 BS13 0.036 0.121 0.090 0.121 0.127 0.549 0.471 0.267 0.172 0.840 1 0.536 BS16 0.164 0.167 0.219 0.306 0.235 0.491 0.561 0.275 0.375 0.571 0.536 1

Undisturbed forest plots (BS1 & BS2) are highlighted in grey and all natural forest plots enclosed within the dotted line (Table 4.2). These constitute a group with similarity indices greater than 39%. Outside this group dissimilarity increases markedly. A second group: Chromolaena (BS16), Jungle rubber (BS10) and Paraserianthes (BS7) exhibit between 14% and 16.5% similarity. Then there is a gradient of increasing disimilarity (10.7% to 3.6%) from Paraserianthes 1(BS6), plantation rubber (BS8) to Cassava and Imperata (BS12 & BS16). The Cassava and Imperata plots are the most similar, and the more mature Paraserianthes plot shows greater similarity with natural forest than the younger Paraserianthes plot.

4.5.3 Cluster analysis:

The analysis resulted in two main groupings: natural forest plots and mono-dominant plots. The natural forest plots are all quite dissimilar. The logged and unlogged plots are included in separate sub-groups. Plantation rubber is grouped with natural forest plots although it is the most dissimilar. Paraserianthes is grouped in the mono-dominated 'agricultural plots'. Of all the plots, Cassava and Imperata are the most similar.

4.6 Discussion:

4.6.1 Review of methods: costs in time and effort per plot (?per taxon):

4.6.1.1 Cost effectiveness:

The protocol employed in this study lends itself to a range of simple descriptive analysis as well as similarity indices. It is not suited to more detailed statistical analysis. As such, the method produces an initial indication of changes to bird diversity caused by increasing degrees of forest modification.

It is highly cost-effective. For each forest plot, two observers could collect data and enter it in to a spreadsheet ready for analysis in one day. In open habitats one observer was sufficient. It is an excellent technique for producing quick results. But it depends closely on the identification skills of the observer; for example in the natural forest plots (BS1-BS5) more than 85% of bird contacts were by call. This generates the need for experienced field ornithologists (who are often quite expensive). Furthermore, if the observers are unfamiliar with the avifauna, they should spend 3-4 days practising identification before starting counts.

4.6.1.2 Limitations and recommendation for modifications to the sampling protocol:

In the case of birds, the protocol had two important limitations: a) variation in sampling area and b) selection of the landscape element to be sampled. The question under investigation concerned the effects of different levels of disturbance on avian diversity. The distance at which birds could be detected varied significantly between plots and species. For example, in primary and secondary forest and jungle rubber plots, sight records were all within 30m but some vocal species could be heard up to 500m or more. In the Cassava and Imperata plots, species could be seen and heard for 1 km or more. Obviously, among the latter there is a greater chance of detecting thinly dispersed species.

Other disciplines sampling at the plot can assume that habitat is homogenous for their sample. This assumption is difficult to meet for birds. Sampling within a fixed area (e.g. 30 m from the plot) would produce very few contacts and as a result increase the required sampling time. Sampling (as we did) all contacts made from the plot overcomes the time consideration, but introduces bias because habitat heterogeneity increases with size of sampling area. Increased habitat heterogeneity correlates strongly with increased species diversity.

The argument that this does not matter because we are sampling the land use is also problematic. It assumes that the plot vegetation type is crucial to defining bird diversity in the land use concerned. This may be true for mature forest, but is not the case for birds in the highly modified sites. For example, in the Cassava plot only 7 of the 25 species were actually

50 recorded in the Cassava. At resolutions below 500m2, scrub and trees associated with access tracks and field edges were important elements, adding species such as Prinia familiaris; Cocomantis merulus; Copsycus solaris and Macronus gularis. At a wider resolution, woodland patches around homesteads and rivers probably accounted for the presence of Psiticinus and Gracula religiousus.

A way round these problems could be to base sampling design on landscape ecological principles [see Forman, 1986 p. 201]. This would involve identifying first landscape units( LUTs) and then landscape elements within each landscape unit. A possible protocol could be to establish plots in the principal vegetation type of each landscape unit (as we did). The time- consuming sampling disciplines (plants, termites, insects, etc.) could confine themselves to these, but those disciplines sampling groups with larger home ranges and greater dispersive abilities would combine standardised sampling of plots with non-standardised sampling of other landscape elements (e.g. searching).

Some benefits of this approach are:

• it maintains the benefit of data comparability across groups provided by the plot reference point; • through knowing what occurs in all landscape elements it enables discussion of the influences of these elements on the plot-based species set, i.e. it accommodates source- sink theory which is expected to become more important with increasing habitat modification and ; • it enables the identification of landscape elements that are critical to maintaining biodiversity values in the overall landscape or land use; • policy interventions arising from studies such as these will have impact at the land use or landscape level.

If we consider the Jambi surveys in this framework, we sampled four landscape types: managed natural forest (primary and logged forest plots); commercial plantation forestry (rubber and Paraserianthes plots); traditional agro-forestry systems (jungle rubber and Chromolaena); and 'frontier' agriculture (Cassava and Imperata). These constitute a gradient of landscape change. Taking the two ends of this gradient as an example, I have summarised in Table 4.3 the variety of landscapes elements represented. It is constructive to think of the functional contribution of each of these elements to the overall biodiversity value of the landscape.

Table 4.3 Landscape elements in Natural forest and Frontier agriculture landscapes

Natural Forest Frontier agriculture

Element Sampled? Element Sampled? Pristine forest Plot Cassava Plot (various types) Logged forest Plot Imperata Plot (various age classes) River - Track with scrub from plot Access road; Bird count Stream with vegetation Extraction tracks - Woodlot/Fruit tree grove birds in flight Log pond Bird count Marshy pond Ducks in flight

51 Camp Bird count Scrub

In the natural forest LUT, we sampled three landscape elements according to the protocol suggested above. These were the logging road and forest edge the log camp, and the log pond. Each of these landscape elements characterises a managed forest estate.

Table 4.4 Species record in key habitats outside the sampled landuse types

Species

Landscape element Total Cum. total Logged Forest 74 74 Unlogged Forest 45 88 Road 35 101 Log pond 12 112 Camp 21 119

Sampling the logging road added 13 species not recorded in the forest interior, and the two highly modified landscape elements accounted for 15% of species in the overall landuse. These added mainly non-forest species, migratory shorebirds in the case of the log pond and garden and night birds in the case of the logging camp (Table 4.4).

4.6.2 Relevance of study at regional and global levels:

The lowland, evergreen, mixed dipterocarp forests of the Sunda-shelf are among the most bird diverse habitats on earth; 291 species of which 164 are Sunda endemics, breed mainly or exclusively in this biome [Wells, 1985 p. 202]. They are also among the most threatened; large areas are being converted to agriculture with the remaining areas being modified by commercial timber extraction. Understanding the effects of this process on the distribution and status of bird species is crucial for planning and prioritising conservation action. This study is an important contribution in this field of investigation.

4.6.3 Relevance to Rapid Biodiversity Assessment:

A World Bank review of Integrated Conservation and Development Project (ICDPs) in Indonesia, identified a lack of clear linkages between the conservation importance of a reserve and rural development activities as a key factor in the under performance of these projects. This is a growing recognition of the need for spatially-referenced biodiversity information during the preparation phase. The protocol employed in this study would appear ideal for providing an understanding of relative biodiversity values of habitats represented in an ICDP area.

4.7 Preliminary conclusions and discussion of results:

Although natural forest habitats form a group distinct from the 'frontier' agricultural sites, there are quite high levels of dissimilarity between plots. This may be a factor of sampling time. The total number of species recorded in the five natural forest plots and forest edge (see Table 4.3) was 112, and the maximum number of species at any one plot was 56. The dissimilarity is believed to be as much a function of short sampling times and small sampling areas, as a real difference between plots. If the number of replicates was increased we would expect more

52 shared species between plots. This conclusion is supported by the species accumulation curves which flatten out slowly. In the open frontier agriculture and plantation habitats, sample effort created less bias, and the similarity indices are more robust.

The bird communities of natural forest plots differ significantly from the commercial plantation and frontier agriculture plots. The latter support non-forest bird communities. Typical forest bird families such as hornbills, trogons and tree-swifts are replaced by typical open-country families such as Ardeidae, Turnicidae and Estrillidae. Within families there are also clear differences. There is little overlap in the species composition of, for example, Pycnonotidae, Timalliidae, Sylviidae between natural and non-natural forest habitats. The Jungle rubber has many species in common with natural forest, but species typical of scrub habitats are also represented.

A point of interest in the Imperata plots was the co-occurrence of two closely related warbler species occupying the same niche. On Sumatra, Cisticola juncidis is typically a species of wet- rice agriculture and is well established. Cisticola exilis is a grassland species, typical of Imperata grasslands. A possible explanation of this co-occurrence is that Cisiticola juncidis, already well established on Sumatra, rapidly colonised these new Imperata grasslands, and Cisticola exilis subsequently moved into the area. Selective logging appears to result in an increase in overall species richness of the forest. This is to be expected because it increases habitat heterogeneity and contrast within the landscape. This is illustrated most clearly by the log pond, which adds migratory shorebirds to the species total. As indicated earlier, the invasive species are widespread and common species of little conservation concern, and the important question is what species drop out rather than how many are added. To properly investigate this question for birds requires more detailed studies. This is because greater mobility (and in some species, longevity) may mask subtle changes in habitat quality.

4.8 References:

Reynolds, R.T., Scott, J.M. & Nussabaum, R.A. Condor 82, 309-313 (1980). Andrew, P. The Birds of Indonesia: A Checklist (Peters' Sequence) (Indonesian Ornithological Society, Jakarta, 1992). Thiollay, J.-M. Conservation Biology 9, 335-353 (1995). Magurran, A.E. Ecological Diversity and its Measurment (Croom Helm, London, Sydney, 1988). Forman, R.T.T. & Godron, M. Landscape Ecology (John Wiley & Sons, New York, 1986). Wells, D.R. in Conservation of Tropical Forest Birds (eds. Diamond, A.W. & T.E.Lovejoy) 213-233 (International Council for Bird Preservation, Cambridge, 1985).

Appendix 4.1 Species excluded from analysis

Number Species Name Plot English Scientific BS6 BS12 626 Silver-rumped Swift Rhaphidura leucopygialis X 633 Whiskered Tree-swift Hemiprocne comata X 701 Wreathed Hornbill Rhyticeros undulatus X

53 SECTION 5: MAMMALS

SURVEY OF MAMMALS ON DIFFERENT LAND USE TYPES

By: Ibnu Maryanto 1), Agus P. Kartono 2), and Martua A H. Sinaga 1) 1)Museum Zoology, Research and Development Center for Biology LIPI, Jl. Raya Bogor Jakarta km 46 Cibinong, Indonesia, 2)Forest Conservation, Faculty of Forestry, IPB, Darmaga, Bogor

5.1 Introduction:

Not less than 620 species mammals are found in Indonesia. According to Krebs (1972), the distribution of animal species generally follows, or is commensurate with, changes in the physical environmental pattern. Medway (1972) believes that changes in animal diversity are congruent with elevation changes. Studies of bats from Kitchener et al., (1990) suggest that populations vary inversely with elevation. Habitat type is the other factor which greatly influences animal diversity according to Kitchener et al., (1997) from their study in Tembagapura, Irian Jaya, [see also Kitchener and Maryanto (1997) from the result study in P.Gag Irian Jaya].

To understand the degree to which mammal species vary across similar habitat types, we need to observe their diversity and distribution from primary forest to industrial forest plantation and jungle rubber as well as in areas that have been converted from forest to open areas such as alang-alang (Imperata) and/ or Cassava garden. This is a report on mammal diversity across six different land-use types : primary forest, secondary forest, jungle rubber jungle and rubber plantation, Paraserianthes plantation, and open areas under Imperata and Cassava (LUTs combined). Our observations were made just a week after a drought had broken and after forest fire and dense smoke affected all land-use types in Sumatra.

5.2 Site selection and methods:

5.2.1 Sites:

The sites were established at Pasir Mayang (01º 04’ 47’’ S, 102º 06’ 02’’ E), Pancuran Gading (01º 10’ 12’’ S, 102º 06’ 50’’ E), and Kuamang Kuning (01º 35’ 56’’ S, 102º 21’ 11’’ E), Muara Bungo, Jambi. There were 11 sample plots co-located with the vegetation survey team across six land use types as follows :

• Primary Forest : (BS1, BS2) • Logged-over Forest : (BS3, BS4, and BS5) • Industrial Forest Plantation (Paraserianthes) : (BS6 and BS7) • Rubber Plantation : (BS8) • Rubber Jungle ; (BS10) • Imperata and Cassava (Open areas) : (BS12 and BS14)

5.2.2 Data collection:

We surveyed the diversity and ecological status of mammals during the period 19-29 November 1997. (Annex III Tables 5,6,7) We used two observer groups: the first group

54 observed bats and rats, and the second group observed other mammals, excluding bats and rats. The second group observed direct and indirect occurrences of mammals at the sample plot location.

5.2.2.1 Bats and Rats:

Collecting data for bat and rat diversity was implemented using mist nets and rat traps on six different land use types. We used a specific rat trap design known as “Trap Kasmin” (28x12x12cm) made from wire. In the field we used baits of coconut and peanut butter to attract the animals. We placed traps five metres apart along a transect on each land use type with 15-20 traps every night. From experience of Kitchener et al., (1997) and from our own experience on small mammal research in Nusa Tenggara and Maluku during 1989-1995, traps should be left for three days, except in an open area such as alang-alang and Cassava (two days only). The total number of traps used for all land use type was 459 (Table 5.1). We trapped bats using a mist net placed to intercept bat flight paths. For each site, we used 2 9x12m mist nets, and left these for three days. We checked the traps every day and night at 08:00 and 20:00hrs (Table 5.1).

The maturity status of bats and rats is based on the basioccipital and basispenoid bone component (Kitchener and Maryanto 1993; Maryanto and Boeadi 1994). We determined the reproduction status for each bat and rat by direct examination of the position of the testes (abdominal, inguinal, or scrotal), virgin uterus form (nulliparous), the total number of foetuses, and the amount of scars that indicated whether or not an animal had been pregnant.

5.2.2.2 Small and large mammals (excluding bats and rats):

Observations of small and big mammals other than bats and rats were made twice daily; the first between 06.30-08.00 and second between 16:30-18:00. To complete the data, we included an additional observation at night between 20.00-22.00hrs. The types of data recorded were: a) animal species, b) total number of individuals, c) the contact distance between animals and the plot centre, d) contact direction, e) time of contact and f) direct and indirect track from foot, sound, and other tracks.

Table 5.1 The total number of traps and mist nets area for each land use type.

Habitat Trap Mist net (m2) Primary forest (BS1&2) 60 163,8 Log over (BS 3) 60 117 Log over 1983 (BS3,4 &5) 60 163,8 Paraseriantes (BS6) 60 156 Paraseriantes (BS7) 60 163,8 Rubber plantation (BS8&9) 60 132,6 Alang-alang (BS12&13) 30 31,2 Cassava plantation (BS14&15) 30 23,4 Rubber jungle (BS10&11) 39 109,2 Total 459 1060,8

55 5.3 Data Analysis:

For all taxa, we made comparisons between diversity from different land-use types and Simpson Index diversity (Simpson, 1949), Shannon-Wiener Index (Ludwig and Reynolds 1988) and cluster analysis using SPSS/PC software.

5.4 Discussion and Results:

5.4.1 Bats:

Mist nets were unsatisfactory. From the total mist net traps with size 9 and 12 meter that were used for three nights on each land use type, there were only a few species captured, which included frugivores such as Rousettus amplexicaudatus at the Paraserianthes site, Cynopterus brachyotis at rubber plantation and Balionycteris maculata at logged-over forest. Other species were Pipistrellus javanicus at a Paraserianthes plantation, Rhinolophus lepidus in logged-over forest ( an insectivore). The total number of individual of bats per square meter during the survey was 0.0122 with 0.005 and for the total species. (Table 5.2). All female bats were pregnant. Also we found Pteropus vampyrus (kalong) that was observed on the flight line in logged-over forest. These animals can fly between islands 60 km apart (Marti Fujita, personal comunication).

Compared with our survey of bats in 1991 (before the El Niño smoke disaster in our study area), Maryanto (unpublished data) recorded 0.08 species m-2 and 0.39 individuals m-2 in logged-over forest. This figure was greatly reduced with the change to Paraserianthes plantation. In 1991 using the mist net with total area 140.4 m2, we recorded Balionycteris maculata, Megaerops wetmoreyi, Cynopterus brachyotis, Macroglotus sobrinus, Rousettus amplexicaudatus, Nycteris javanica, Tylonycteris pachypus, Tylonycteris robustula, Hipposideros cervinus, Megaderma spasma and Rhinolophus sp. (cf. lepidus) (Table 5.3). Another comparison with data from logged-over forest at selection cutting in Serestra II, Bangko, Jambi Province, in January 1996 showed that the total number of individual and species for bats per square meter was 0.029 and 0.009 (Maryanto et al., 1996) (Table 5.3). The differences between diversity and abundance may be because of the excessive smoke in the current survey area. The site location in this survey was particularly bad due to smoke that only disappeared one week prior to the survey. We were surprised not to find common species such as Cynopterus brachyotis or Macroglossus sobrinus. These species are usually very common in all areas of Sumatra with elevation less than 1000m (Kitchener et al., 1990, Corbet and Hill 1992). We conclude that the widespread smoke may have had a negative impact on the bat populations.

Using mist nets should be much more efficient to catch frugivorous bats (Megachiroptera), but on this survey we were unsuccessful. On the other hand for a mist net that usually is not effective to catch Microchiroptera (insect eater), we recorded a higher than usual percentage of 27.27% (3 individuals from a total of 11 bats). The impact of smoke in these area is the main reason why the total population of frugivorous was less, but this did seem to affect insectivores. The high percentage of these bats is related to their feeding behaviour, as they can catch something small without using their eyes. Another possibility is the nature of their habitat. Insectivores usually stay in an enclosed close place such as roof or cave, but this is the opposite for the frugivores, who inhabit open spaces (Kitchener et al.,, 1990, Corbet and Hill, 1992).

56 From the total number of bats recored, Megaerops wetmoreyi was the only rare species (Micklenburh et al., 1992). This is a common species in Riau and also in Pasir Mayang, particularly in primary forest and logged-over forest, but recently this forest has been converted to industrial forest plantation (Paraserianthes /BS7) (Maryanto, unpublished data). We suggest another study should be implemented to examine the impact of both smoke and habitat conversion from logged-over to industrial forest plantation.

The species Balionycteris maculata that we trapped in the field indicates that this species is distributed across all primary and secondary forests at elevations below 600m. This might be a new species because it is differs from the one recorded in Kalimantan (Maryanto, unpublished data). To confirm this, we need to compare it with the specimen original recorded in Malaya and now available in the Raffles Museum, Singapore.

Table 5.2 Trapping bats using a mist net for each land use type, individual, individual/trial 100m2*

Land Use Type and plot Effort Species Individual Ind/effort Diversity Primary BS1 &BS2 163.8 - - - - Logged-over forest (BS 3,4,5) 280,8 2 2 0,7 0,3 Paraserianthes (BS6&7) 319,8 2 4 1,3 0,3 Jungle Rubber 241,8 1 5 2,1 0 Alang-alang 31,2 - - - - Cassava plantation 23,4 - - - - Diversity 0,6 * Values are Simpson's diversity index.

57 Table 5.3 Comparative effort in trapping bats at logged-over forest in Serestra, Bangko, Jambi, January 1996 (Maryanto et al. 1996), logged-over forest / primary October 1991 (before conversion to Paraserianthes / location BS7/ Maryanto, I. Data unpublished) and at Pasir Mayang research site, Kuamang Kuning, Pancuran Gading November 1997).

Species Name Bangko Logged-over (1991) Pasir Mayang, (1996) (Now Paraserianthes Kuamang kuning, /BS7) Pancuran Gading,1997 Balionycteris maculata, x x x (BS4,5) Cynopterus brachyotis x x x (BS8,9,10,11) Chironax melanocephalus, x Dyacopterus spadiceus x Megaerops wetmorei, x Megaerops ecaudatus x Penthetor lucasi x Macroglosus sobrinus x x Eonycteris spelaea x Rousettus amplexicaudatus x x (BS6) Hipposideros cervinus, x Nycteris javanica, x Megaderma spasma x Myotis muricola x (BS 7) Rhinolophus lepidus x x (BS4,5) Tadarida mops x Tylonycteris pachypus, x Tylonycteris robustula, x

5.4.2 Rats:

This kind of animal can be found on different land use types and shows differences in degraded habitats. Maxomys rajah usually dominates primary forest and logged-over forest, while Maxomys whiteheadi is normally very common in open areas. But in our survey we found this species in the logged-over forest, Paraserianthes, and rubber plantation. It is very closely related to Rattus exulans that we found also in Paraserianthes and rubber plantations. Although rats occurred across all LUTs, abundance was greatest at the Imperata site (see Table 5.4).

The reproduction status and ecology for each rat species in different land uses can be explained by the following :

5.4.2.1 Microbiogeography of rats:

The dissimilarity distance between locations based on rat habitat show that there are two groups; primary and logged-over forest in the first group, and jungle rubber, Paraserianthes and Imperata in the second group. Results based on dissimilarity distances between rats show that Rattus tanezumi and Rattus tiomanicus tend to share a similar habitat. This is different to

58 Maxomys rajah, that tends to live separately from other rats. The choice of habitat and associated breeding condition for each species can be explained by the following :

Rattus tanezumi (Indonesian black rats) During the survey only one individual was found in the Paraserianthes plantation. This species is similar to Rattus rattus, but there are significant genetic differences between these species (Musser & Carleton, 1993). The reason it is found in Paraserianthes plantation is because there is a lot of human activity in that area. We predicted there would be a close relationship between rats at the Paraserianthes plantation and the level of human activity within the plantation forest.

Rattus exulans (Pacific rats) Ecology: This species has a wider range of distribution in South East Asia, Indonesia, New Zealand and Polynesia and has a specific habitat in Papua New Guinea and Solomon Islands. It is commensal with human activity and can survive up to 3000m above sea level. In paddy fields and gardens this species could become a pest (Maryanto, data unpublished). In this rapid survey the species was found in the Paraserianthes plantation, rubber plantation, Imperata, and jungle rubber. We did not find it in the Cassava plantation, and we suggest that this species might have moved to Imperata- dominated areas.

Reproduction: We caught nine female rats in six different land use types during the survey and they were all pregnant . One female rat found at the Paraserianthes site was pregnant with two scars at both sides and uterine horn 1,48mm wide; another female rat found at the Imperata site was also pregnant with 5 foetuses in the left side and 1 at the other. Dwyer (1975) mentions that this species will breed during the wet season and produce a large litter.

Rattus tiomanicus (Malayan field rats) Ecology: This species was found at both the Paraserianthes plantation and Imperata and it prefers a bushy place (Payne et al., 1985). From nine rats found during the survey, 8 occurred in Imperata , and one at a site similar to Paraserianthes. Corbet and Hill (1992) argue that this species is mutually incompatible with Rattus tanezumi.

Reproduction: Six out of nine rats were male and ready for breeding. From 3 female rats, 2 were pregnant with one soon to be pregnant. About 5-7 babies will be born from each rat.

Maxomys rajah (Brown spiny rats) Taxonomy: Two sub-species of these rats are widespread in Sumatra (Van Strien 1986) : M.r. pellax and M.r. similis. At our site location we are not certain whether it is M.r. similis, so we need to repeat our effort by making a comparative sample from Aceh (Chasen 1940).

59 Ecology: This species tends to live in secondary forest or primary forest with sandy soil (Payne 1985). In this survey, even though the soil type between Paraserianthes and rubber plantation was similar (ultisol) and the sites closer to each other, in this survey we did not find Maxomys rajah in both sites. It was predicted that besides soil type, the litter depth at primary and logged-over forest would exert a major influence over the presence of this species.

Reproduction: Seven mature rats were found in the field from the total specimens. There were 4 male Maxomys rajah from both primary and secondary forest ready for breeding (Scrotal). One female rat had been pregnant twice with the six scars on the right and five on the left (on the logged-over area), and another 2 female rats were found in the primary forest , one pregnant and the other still young but ready for breeding.

Maxomys whiteheadi (Whitehead’s rat) Ecology: This species can be found near the forest. Payne et al., (1985) mentions that this species can attack paddy rice, especially where paddy fields are surrounded by forest. It is associated with Rattus argentiventer, Rattus exulans, Rattus tiomanicus, Rattus tanezumi (Maryanto, data unpublished). In this survey, we found this species in Paraserianthes, logged-over forest, rubber plantation, and Imperata.

Reproduction: We found 3 male rats in the field with imperfect testes (inguinal), only one showing perfect testes (scrotal). Another had just given birth and another 2 were pregnant.

Table 5.4 Records of rats for each land use type and each plot based on sexual status and reproduction.

Habitat Trap Male Male Female Female Trapped Trapping/ trial mature young mature young trial Primary forest (BS1&2) 2 2 2 4 2 Logged-over (BS 3) 2 1 1 2 1 Logged-over 1983 (BS4 &5) 2 2 1 1 4 2 Parasarianthess (BS6) 2 4 2 6 3 Parasarianthes (BS7) 2 1 1 2 1 Rubber plantation (BS8&9) 2 2 - 1 - 3 1,5 Imperata (BS12&13) 1 6 3 2 2 13 13 Cassava (BS14&15) 1 ------Jungle Rubber (BS10&11) 1,3 - 1 1 0,76 Total 16 4 13 2 35

60 Figure 5.1. Relationship between habitat type based on rats species

A. Dissimilarity Index

0 5 10 15 20 25 +------+------+------+------+------+

PF +------+ LOA + | OA +------+ | RP + ------|------+ IFP +------+ Note: PF = Primary forest, LOA = Logged-over area, OA = Open area Imperata, Cassava, RP = Rubber plantation, IFP = Industrial forest plantation

Figure 5.2. Relationship of rat species based on an ideal habitat

B. Dissimilarity Index

0 5 10 15 20 25 Label +------+------+------+------+------+ Rt +------+ Rtan + +------+ Re ------+ |------+ Mw ------+ | Mr ------+

Note. Rt = Rattus tiomanicus, Rtan = Rattus tanezumi, Re = Rattus exulans, Mw = Maxomys whiteheadii, Mr= Maxomys rajah

5.4.3 Other Mammals (excluding rats and bats):

5.4.3.1 Species Richness:

From all land-use types sampled in this survey, the greatest richness of mammals, excluding rats and bat occurred in logged-over forest and jungle rubber. The total percentage of mammals in this area is 45%. If we compare to primary forest as baseline indicator (Alikodra 1990), the species richness in logged-over forest or rubber jungle is greater by 28.57%. This indicates that several mammals, excluding rats and bats, prefer an area with medium crown cover. Larger mammals such as pig (Sus scrofa; babi hutan) and deer (Rusa sambar) can be found at Imperata and Cassava plantation where the species richness has decreased to 71.43%. In the rubber plantation, the richness of big mammals is less than in industrial forest plantation or open area. This may be due to differences in food source and other habitat components (Table 5.5). Jungle rubber is a very good alternative habitat for large mammals because there is almost no treatment of plants and the vegetation is not homogeneous. This habitat will provide a wide variety of local habitats for different plant types. Hence, the component of the habitat is still complete. One mammal has been identified, T. cristatus (lutung budeng) with a black and white colour pattern that facilitates studying the social relationship between them. Using the Shannon Index of diversity (Ludwig and Reynold 1988), we see that the percentage of jungle rubber, 19.58% (Table 5.6), is higher than primary forest. The differences based on community

61 similarity index between two locations is about 23.53%. It seems that species diversity in jungle rubber is higher than primary forest. There would be changes of 60.45% if primary forest were to change to an open area such as alang-alang or Cassava (Figure 5.3).

2.5

1.5

0.5

0 PF LOA IFP JR RP OA

Figure 5.3 .Shannon similarity index changes base on habitat type

Community Similarity Index The community similarity index tends to be high between primary forest and logged-over forest (about 55.56%). This index can show the effect of conversion from primary forest to logged- over forest and indicates an impact on dominant changes on mammals of about 44.44%.

Table 5.5 Mammals species excluding rats and bats in the study area

No. English name Species PF LOA IFP JR RP OA 1 Agile gibbon Hylobates lar agilis X X 2 Banded langur Presbytis melalophos X X 3 Banded palm civet Hemigalus derbyanus X 4 Barking dear Muntiacus muntjak X 5 Bearded pig Sus barbatus X X X X X 6 Common treeshrew Tupaia glis X 7 Domestic pig Sus scrofa X 8 Giant squirrel Ratufa affinis X 9 Horse-tailed squirrel Sundasciurus hippurus X 10 Large treeshrew Tupaia tana X 11 Leopard cat Prionailurus bengalensis X 12 Lesser mouse-deer Tragulus javanicus X 13 Long-tailed macaques Macaca fascicularis X X 14 Low's squirrel Sundasciurus lowii X 15 Plantain squirrel Callosciurus notatus X 16 Prevost's squirrel Callosciurus prevostii X X X X 17 Sambar deer Cervus unicolor X X 18 Silvered leaf monkey Trachypithecus cristatus X X X 19 Sun bear Helarctos malayanus X X 20 Whiskered flying squirrel Petinomys genigarbis X Total Individual (n) 32 63 3 12 9 4 Total species (S) 7 9 3 9 4 2 Percentage of species distribution (%) 35.00 45.00 15.00 45.00 20.00 10.00 Conversion of primary forest (%) - 28.57 -57.14 28.57 -42.86 -71.43

62 Microbiogeography: A dissimilarity measure based on mammals species in six land-use types, suggests there are 3 groups of habitat from the identified 20 mammals that can be used as future indicators for mammals. The first group is Paraserianthes, alang-alang, Cassava plantation, and rubber plantation. The second group is jungle rubber, and the last group is primary forest and logged-over forest (Figure 5.4). Classification of habitat type is almost similar with microbiographical classification on habitat type of rats (see Figure 5.1 and 5.2).

Table 5.6 Species Diversity index on different land use types

No. Species PF LOA IFP JR RP OA n H' n H' n H' n H' n H' n H' 1 Hylobates lar agilis 7 0.333 15 0.342 2 Presbytis melalophos 5 0.290 10 0.292 3 Hemigalus derbyanus 1 0.108 4 Muntiacus muntjak 1 0.066 5 Sus barbatus 2 0.173 2 0.110 1 0.366 1 0.207 1 0.244 6 Tupaia glis 1 0.207 7 Sus scrofa 2 0.347 8 Ratufa affinis 1 0.366 9 Sundasciurus hippurus 1 0.207 10 Tupaia tana 1 0.207 11 Prionailurus bengalens 1 0.244 12 Tragulus javanicus 1 0.066 13 Macaca fascicularis 12 0.368 17 0.354 14 Sundasciurus lowii 1 0.207 15 Callosciurus notatus 1 0.207 16 Callosciurus prevostii 3 0.222 3 0.145 3 0.347 2 0.334 17 Cervus unicolor 1 0.207 2 0.347 18 Trachypithecus cristatu 13 0.326 2 0.299 5 0.326 19 Helarctos malayanus 2 0.173 1 0.366 20 Petinomys genigarbis 1 0.066 Total individual (n) 32 63 3 12 9 4 Total species (S) 7 9 3 9 4 2 Shannon Index (H') 1.667 1.765 1.099 2.095 1.149 0.694

The habitat type indicates the importance of the density of tree and crown cover, but the most important influence is availability of food. The first location, which includes alang-alang, Cassava plantation, Paraserianthes, and rubber plantation, tends to have more open habitat types. There are 4 group of animals which occupy 3 habitat types within and between which they interact. The 4 groups are as follows :

Group 1 : Tragulus javanicus, Petinomys genigarbis, Muntiacus muntjak, Presbytis melalophos, Macaca fascicularis, Hylobates lar agilis. Group 2 : Ratufa affinis, Helarctos malayanus, Hemigalus derbyanus,

63 Group 3 : Sundasciurus lowii, Callociurus notatus, Tupai glis, Sundasciurus hippurus, Tupaia tana, Cervus unicolor, Sus scrova, Prionailurus bengalensis, Group 4 : Callociurus prevostii, Trachypithecus cristatus, Sus barbatus (Figure 5.5).

5.5 Function of food:

For the 4 groups of mammals above, each group can not necessarily live together on the same spatial distribution of food. Even if we find several mammal who live together at the same habitat, they do not necessarily compete for food because they have different spatial ranges. Mammals species identified in this survey were mostly from the group that eat fruits (38.10%), followed by seed-eaters (14.29%) and then grass or root tuber eaters (9.52%). The source of food consumption is outlined in Table 5.7. The food source can also be used to predict the Hylobates lar as an indicator of forest condition. There are 5 groups of animals, based on spatial distribution, that are dispersed across different elevations 0-1, 1-3, 3-15, 15-30, >30 meter (Table 5.8).

Figure 5.4 Relationship between type of habitat base on mammals species excluding rats and bats.

Dissimilarity Index 0 5 10 15 20 25 +------+------+------+------+------+ IFP -+--+ OA -+ +------+ RP -----+ +------+ PF ------+------+ | LOA ------+ | JR ------+

Note: IFP= Paraserianthes plantation, OA= Open Area / Imperata, Cassava, RP= Rubber Plantation, PF= Primary Forest, LOA= Logged-Over Forest, JR= Jungle Rubber

64 Figure 5.5 Relationship between species mammals based on an ideal habitat.

Dissimilarity Index

0 5 10 15 20 25 +------+------+------+------+------+ Tj -+ Pg -+------+ Hd -+ +------+ Pm -+ | | Mf -+------+ + ------+ Hl -+ | | Ra ------+------+ | | Hm ------+ +------+ | Hd ------+ | Sl -+ +------+ Cn -+ | | Tg -+------+ | | Sh -+ +------+ | | Tt -+ | +-----+ | | Cu ------+ | +------+ | Ss ------+ | | Tj ------+ | Cp ------+------+ | Tc ------+ +------+ Sb ------+

Note: Tj=Tragulus javanicus, Pg= Petinomys genigarbis, Mm= Muntiacus muntjak, Pm= Presbytis melalophos, Mf= Macaca fascicularis, Hl= Hylobates lar agilis, Ra= Ratufa affinis, Hm= Helarctos malayanus, Hd= Hemigalus derbyanus, Sl= Sundasciurus lowii, Cn= Callociurus notatus, Tg= Tupai glis, Sh= Sundasciurus hippurus, Tt= Tupaia tana, Cu= Cervus unicolor, Ss= Sus scrova, Pb= Prionailurus bengalensis, Cp= Callociurus prevostii, Tc= Trachypithecus cristatus,Sb= Sus barbatus.

65 Table 5.7 Potential sources of food for mammals Food source No Species Arthropoda Warm Bee nest TerMites Leave Young leave Fruit Seed Grass Ground tree Shrub Mushroom Small animals Vegetables Insect 1 H. lar x xx xxx 2 P. melalophos xx xx xxx 3 H. derbyanus xx xxx x 4 M. muntjak xx x x x xxx 5 S. barbatus xx xxx xx xx x 6 T. glis xxx xx x 7 Sus scrofa xxx xx xx 8 R. affinis x xxx 9 S. hippurus x xx xxx 10 T. tana xxx xx x 11 P. bengalensis xx xxx 12 T. javanicus xx xxx x 13 M. fascicularis x xx xx xxx x 14 S. lowii xx xxx x 15 C. notatus xx xxx 16 C . prevostii xx x xxx 17 C. unicolor x x xxx xx xx 18 T. cristatus xxx xx x 19 H. malayanus xxxxx x xx 20 P. genigarbis x

66 Table 5.8

Spatial distribution of species according to food source

Elevation No. English Name Species meter 0 - 1 1 Banded langur (BW) Presbytis melalophos 2 Banded palm civet Hemigalus derbyanus 3 Barking deer Muntiacus muntjak 4 Bearded pig Sus barbatus 5 Domestic pig Sus scrofa 6 Lesser mouse-deer Tragulus javanicus 7 Large treeshrew Tupaia tana 8 Low's squirrel Sundasciurus lowii 9 Leopard cat Prionailurus bengalensis 10 Sambar deer Cervus unicolor 11 Sun bear Helarctos malayanus 1 - 3 1 Common treeshrew Tupaia glis 2 Horse-tailed squirrel Sundasciurus hippurus 3 Long-tailed macaques Macaca fascicularis 3 - 15 1 Plantain squirrel Callosciurus notatus 2 Prevost's squirrel Callosciurus prevostii 3 Silvered leaf monkey Trachypithecus cristatus 4 Whiskered flying squirrel Petinomys genigarbis 15 - 30 1 Banded langur Presbytis melalophos 2 Giant squirrel Ratufa affinis >30 1 Agile gibbon Hylobates lar

5.6 Recommendations: • Conversion to monoculture areas such as alang-alang, Cassava, industrial forest plantation (Paraserianthes), and rubber plantation will cause a decrease in species richness. Hence, it is important to maintain an area that includes natural mixed forest. • T. cristatus found in the field has a different colour pattern; we need further study of its ecological status. • Bats are potentially useful pollinators and agents for pest control. The smoke hazard may have caused several animals to either die or to migrate to alternative habitats. We need further research about this. • We found one bat taxon that may represent a new species. To confirm this will require comparison with collected specimens from Malaya in the Raffles Museum, Univ. of Singapore.

UCAPAN TERIMA KASIH

Penulis mengucapkan terima kasih pada CIFOR sebagai penyandang dana pada penelitian ini dibawah pipimpinan proyek Dr. Andi Gillison, dan Ir. Nining Liswanti sebagai koordinator selama di lapangan. Tidak lupa pula kami ucapkan terima kasih pada Bapak Sidam sebagai pengemudi, Burhanudin dan Aswandi sebagai pembantu lapangan selama penelitian berlangsung.

5.7 References”

Alikodra, H.S. (1990). Pengelolaan satwa liar I. PAU-Ilmu Hayat, IPB. Dirjen Perguruan Tinggi, Departemen P dan K. Bogor 303 pp. Corbet, G.B. and Hill, J.E. (1992). The mammals of the The Indomalayan region: A systematic review. Natural History Museum Publications 488 pp. Chasen, F.N. (1940). A Handlist of Malaysia Mammals. A systematic list of the mammals of the Malay Peninsula, Sumatra, Borneo and Jawa, including the adjacent small islands. Bull. Raff. Mus. Straits Settlements 15: 1-209. Dwyer, P.D. (1975). Observation on the breeding biology of some New Guinea murid rodents. Australian Wildlife Research 2: 33-45. Kitchener, D.J., Boeadi,Charlton, L. and Maharadatunkamsi. (1990). Wild Mammals of Lombok Island. Rec. West. Aust. Mus. Supplement. 33. 1-129. Kitchener, D.J. and Maryanto, I. (1993). Taxonomic reappraisal of the Hipposideros larvatus species complex (Chiroptera, Hipposideridae) in the Greater and Lesser Sunda islands, Indonesia. Rec. West. Aust. Mus. 16: 119-173. Kitchener, D.J. and Maryanto, I. (1997). Preliminary mammal survey of Gag island, Irian Jaya Province, Indonesia, 10-20 July 1997. Kitchener, D.J., Boeadi and Sinaga, M.H. (1997). The mammlas of the Freeport Contract of Work Region , Irian Jaya: results from the survey of 14 February-6 March 1997. Unpublished report to Freeport, Jakarta. Krebs, J.C. (1972). Ecology: the experimental analysis of distribution and abundance. Harper & Row Publisher, New York. Ludwig, J.A. and Reynolds, J.F. (1988). Statistical Ecology. A primer on methods and computing. John wiley & Sons. 337 pp. Maryanto, I and Boeadi (1994). New record of the Highland Blossum bat, Syconycteris hobit Ziegler, 1982 (Mammalia: Chiroptera: Pteropodidae) from Irian Jaya, Indonesia. Raff. Bull. Zool. 42: 521-520. Maryanto, I., Yusuf. dan Marakarmah, A. (1996). Keanekaragaman jenis-jenis hewan pada mintakat penyangga di Kawasan HPH. PT. Serestra II Jambi. Medway, L. 1972. The Gunung Benom Expedition (1967). The distribution and altitudinal zonation of birds and mammals on Gunung Benom. Bull. British Museum (Natural History) 23: 103-154. Micklenburgh, S.P., Hutson, A.M. and Racey, P.A.. (1992). Old World Fruit Bats, An Action Plan for their Conservation. IUCN/ SSC Chiroptera Specialist Group. 252 pp. Musser, G.G. and Carleton, M.D. (1993). Muridae. D.E. Wilson & D.M. Reeder (Editor), Mammals species of the world: a taxonomic and geographic reference. Smitsonian Istitution Press: Washington D.C. Payne, J., Francis, C.M. and Phillipps, K. (1985). Field guide to the mammals of Borneo. The Sabah Society with World Wildlife Fund Malaysia. 329pp Simpson, E.H (1949). Measurements of diversity. Nature 163:688

SECTION 6: CANOPY INSECTS

CANOPY ARTHROPODS AND BUTTERFLY SURVEY: PRELIMINARY REPORT

Allan D Watt1 and Paul Zborowski2 1Institute of Terrestrial Ecology, Edinburgh Research Station, Bush Estate, Penicuik, Midlothian EH26 0QB, Scotland, UK; 2PO Box 867, Kurunda, QLD 4872, Australia

6.1 Introduction:

Arthropod diversity in tropical forests represents a concentration of biodiversity locally, regionally and globally. Arthropods carry out many significant ecosystem processes, notably decomposition, herbivory and pollination, and they also represent a food source for many vertebrate species. A few Lepidoptera are even a direct source of income in parts of Indonesia and elsewhere. Forest clearance, whether partial or complete, represents a major threat to arthropod diversity. The size of this threat is, however, unknown, as are the consequences for ecosystem 'health'. Theoretical predictions of species extinctions as a result of forest clearance are no substitute for direct measurement of biodiversity. Among the many problems with theoretical predictions based on supposed species-area relationships is the assumption that areas, once cleared of forest, are no longer suitable for any species. This is clearly untrue - secondary forest, plantations of rubber and other tree species and other types of land use all have the potential to contain many arthropods and other species. We are clearly correct in assuming that some forms of land use have lower levels of biodiversity than intact forest, but few studies have actually measured the effect of land-use change on biodiversity. The main problem with actually measuring biodiversity (and the attraction of theoretical approaches) is that biodiversity is impossible to measure in its entirety. Rapid biodiversity assessment methods have therefore been developed to allow comparisons of biodiversity in different places, or the same place at different times, by sampling a subset of biodiversity in a statistically rigorous way in as short a time as possible. The lowland forests of Sumatra have been logged and cleared to such an extent that very little intact forest remains. Clearance has resulted in a range of land uses including secondary forest, rubber plantations and Imperata grassland. The effect on biodiversity of such dramatic changes in land use is not known, and there is an urgent need to develop a rational strategy for the conservation of biodiversity. This strategy might entail the protection of remaining fragments of intact forest or the promotion of alternative land uses which are both productive and rich in biodiversity. Such a strategy requires a much better understanding of the variation in biodiversity in the mosaic of land uses in lowland Sumatra.

As part of an overall programme to carry out a rapid biodiversity assessment in the Jambi Province of Sumatra, assessment of arthropod diversity was measured in several ways. This report describes canopy arthropod and butterfly surveys. Separate reports describe termite diversity assessment (Jones et al. 1988), light trapping and general insect survey. The aim of this part of the project was to assess the impact of logging and other land use changes on the diversity of arthropods in central Sumatra. Canopy arthropods were surveyed because previous studies indicate that arthropod diversity reaches a maximum in the canopies of tropical forest trees. However, few studies have compared the diversity of arthropods in the canopies of intact forest and plantation trees. Butterfly surveys were carried to provide a comparison of insect diversity in all sites -- several land-use types surveyed had no tree canopy.

69 The following land use types were surveyed: • Intact forest • Logged forest • Secondary forest • Jungle rubber • Rubber plantation • Paraserianthes plantation • Cassava fields • Chromolaena fallow • Imperata grassland Full site descriptions are given by Gillison et al. (this report).

6.2 Aims and objectives:

The aim of this project was to assess the impact of logging and other land use changes on the diversity of arthropods in central Sumatra and so provide baseline data for biodiversity assessment based on arthropod diversity.

The objectives of this project were:

• To assess the abundance and diversity of the canopy arthropod community of selected land-use types. • To assess the abundance and diversity of the butterfly community of selected land-use types.

6.3 Personnel:

Allan D Watt - Institute of Terrestrial Ecology, Scotland, UK Paul Zborowski - Kuranda, Australia C Noor Rohmah - CIFOR, Indonesia

6.4 Methods:

6.4.1 Review of existing methods:

6.4.1.1 Introduction:

A wide range of methods is used to assess the diversity of insects in tropical forests and other habitats. These include canopy fogging and butterfly transects; the two methods used here and discussed below. Other methods include a) general collecting, b) ground quadrat or transect sampling (as used to estimate termite and ant diversity (Jones et al.1988)), c) light trapping, and d) Malaise and flight interception trapping. These methods are discussed at length elsewhere and will not be described in detail here. It is worth pointing out that some methods are more suitable for collection of specimens for taxonomic work, and other methods are more suitable for biodiversity assessment. The key requirement for the latter is comparability. Many methods do not provide data that are comparable or are only comparable after analytical methods, which are not designed for biodiversity assessment (such as rarefaction). Broadly speaking, techniques which employ standardised sampling across transects or grids are the best methods and general collecting over non-standardised time periods are the worst. Trapping techniques should also be avoided where possible (at least in rapid surveys) because unless the

70 traps are used under the same environmental conditions (e.g. cloud cover and moonlight for light trapping), the results will not be comparable. Additional problems exist for 'passive' traps, such as Malaise traps, whose catches are affected by the degree to which they are located in open (e.g. grassland) or closed habitats (e.g. intact forest). This is a problem which may not be overcome, and results may be obtained which reflect the movement of insects within and through plots, rather than the diversity of insects within them.

6.4.1.2 Canopy arthropods:

The sampling or canopy arthropods has only been made possible through the development of canopy fogging methods. A full review of canopy sampling is given by Stork, Adis and Didham (1995). The techniques as applied in this project are outlined below.

6.4.1.3 Butterfly sampling:

A number of different approaches can be used to sample butterflies, including trapping and netting, but perhaps the most significant development in butterfly survey has been the use of 'butterfly walks' (Pollard and Yates 1993). This method was originally developed for European species and has proved useful in quantifying temporal and spatial trends in the abundance and diversity of species in the UK and elsewhere (e.g. Pollard, Moss and Yates 1995). It has now also been adapted for use in tropical forests (e.g. Hill et al. 1995, Watt et al. 1997, Lawton et al. 1998, Stork et al. in prep.).

6.4.2 Field methods used on this survey:

6.4.2.1 Plot locations:

Two plots were chosen in each land-use type, apart from Chromolaena fallow and secondary forest (where single plots were surveyed). The plots in intact forest, logged forest and Paraserianthes plantation were distinct enough spatially to be regarded as replicates for the arthropod survey, but the rubber plantation, jungle rubber, Cassava and Imperata plots were situated very close to each other and should be regarded as 'psuedo-replicates'. The data from these plots have not been combined for the purposes of this report, but this problem is discussed below.

6.4.2.2 Canopy fogging:

Canopy fogging was done at all 11 plots with tree canopies, i.e. the forest and plantation plots. A 'King' fogger [specifications available] was used to fog the canopy with a pyrethroid-based insecticide diluted with diesel. In each plot, apart from the jungle rubber plots, 25 1m2 collecting trays were suspended on ropes strung between trees. In each of the jungle rubber plots, 20 trays were used. The collecting trays were placed within, or close to, the 4×50 m transects used for the plant surveys (Gillison et al. 1988). Approximately two trays were placed under each tree so that about twelve trees were fogged in each plot. A collecting bottle was attached to each tray with approximately 2 cm of 70% alcohol. A pre-printed label was placed in each collecting bottle identifying the location of each sample. One hour after fogging, the trays were washed down with 70% alcohol and the collecting bottles (and trays) were removed. The arthropod samples were then cleaned, transferred to glass tubes and sorted as described below.

71 6.4.2.3 Butterfly transects:

Butterfly transects were done in all plots, i.e. those with and without tree canopies. Two butterfly transects were sampled in each plot, so that each of the two available recorders could work independently without disturbance. Each transect comprised about half the ‘plant’ transects plus another 25m. The observers walked up and down their transect for 30 minutes and then moved to the other transect. Thus each plot was surveyed for two person-hours. The observers attempted to catch any butterfly flying close to them. Captured butterflies were placed in paper envelopes on which were written the date, time, plot number and recorder. Butterfly abundance in each plot was measured by additionally recording the number of butterflies seen and not caught. All butterflies were identified to family level in the field. Captured butterflies were removed for sorting to morpho-species.

6.4.3 Analysis: 6.4.3.1 Canopy fogging:

Canopy fogging was used to assess the abundance of arthropods in different orders and the diversity of ants, spiders and beetles. Thus, during the field survey ants, beetles and spiders were removed from each sample, the samples were fully sorted to order, and the abundance of each group recorded.

The aim of post-field survey work is to: 1. Sort the ants, beetles and spiders to morpho-species. 2. Sort all samples to order.

For this report, preliminary analyses on the data were carried out as described in the results section. After sorting to morpho-species, further analyses were being carried out, including estimation of species richness (Colwell and Coddington and Coddington 1994) and comparison of the species composition of different sites (Krebs 1989).

6.4.3.2 Butterfly transects:

It was concluded that there were insufficient individual butterflies caught, as a result of the time available for surveying butterflies, to allow comparisons of species richness and composition. Analysis of butterfly abundance, including the abundance of different families of butterflies was carried out.

6.4.4 Data storage and access:

At present copies of all data are held by the consultants and CIFOR. Long-term arrangements for data storage and access will be made during early 1998. (Annex III, Table 8,9,10)

6.5 Preliminary results:

6.5.1 Canopy arthropod abundance:

Total arthropod abundance The mean number of arthropods varied from about 20 to 290 arthropods m-2 (Figure 6.1). Arthropods were most abundant in one of the jungle rubber plots (BS11) and least abundant in

72 one of the Paraserianthes plots (BS6). Note that Figure 6.1 and subsequent figures show mean abundances and standard errors.

Abundance of different arthropod groups Table 6.1 shows the average number of arthropods in each of the groups sorted to order (or family). Note that all the data discussed below are from partial sorting, apart from the ants, beetles and spiders, and must be considered to be preliminary. The most abundant groups were the ants and the termites, on average 32 and 21 m-2 , respectively. Together these two groups made up 67% of the total number of arthropods sampled. The next most abundant groups were the Coleoptera, Diptera, Hemiptera, Thysanoptera, and spiders (Araneae). Together these group, plus the ants and termites, made up 85% of the total number of arthropods. Psocoptera, Hymenoptera (other than ants), Collembola and several other groups made up the remaining 15%. Each group is considered separately below.

Ants Not surprisingly, the pattern of abundance of ants in different plots was very similar to the pattern of abundance of total arthropods (Figure 6.2). Ants were notably abundant in the jungle rubber plots (BS11 in particular), one of the intact forest plots (BS1), and the secondary forest plot. Ants were notably few in numbers in the rubber plantation plots.

Termites Termites were the most patchily distributed group across the different plots (Figure 6.3). They were only recorded in four plots and abundant in only two of those: one of the intact forests plots (BS2), and one of the logged forest plots (BS5).

Table 6.1 The mean and percentage abundance of different arthropod groups recorded from canopy fogging at Pasir Mayang area, Jambi, Sumatra Nov. 1997.

Order Mean Percentage Ants 31.7 32.7 Termites 20.7 21.3 Coleoptera 8.3 8.6 Diptera 6.4 6.6 Hemiptera 5.5 5.7 Thysanoptera 5.2 5.4 Spiders 4.6 4.7 Psocoptera 4.4 4.5 Hymenoptera 3.5 3.6 Collembola 2.7 2.8 Lepidoptera 1.5 1.6 Acari 0.9 0.9 Orthoptera 0.8 0.8 Blatodea 0.5 0.6 Neuroptera 0.1 0.1 Total 97.0 100.0

73 Coleoptera Coleoptera were most abundant in the jungle rubber plots and one of the rubber plantation plots (Figure 6.4). Elsewhere, they were more-or-less equally abundant.

Diptera Diptera were most abundant in the rubber plantation plots, the jungle rubber plots, one of the logged forest plots (BS5) and one of the Paraserianthes plots (BS7) (Figure 6.5).

Hemiptera Hemiptera were more abundant in the logged and secondary forest plots, the jungle rubber plots and one of the Paraserianthes plots (BS7) than elsewhere (Figure 6.6).

Thysanoptera Thysanoptera were particularly abundant in the jungle rubber plots and one of the logged forest plots (BS4) (Figure 6.7).

Spiders Spiders were most abundant in one of the jungle rubber plots (BS11) and more or less evenly abundant elsewhere (Figure 6.8).

Psocoptera Psocoptera were most abundant in one of the jungle rubber plots and least abundant in one of the Paraserianthes plots (BS7) (Figure 6.9).

Hymenoptera Hymenoptera other than ants were notably abundant in one of the jungle rubber plots (BS11) and notably few in number in one of the Paraserianthes plots (BS6) and one of the intact forest plots (BS2) (Figure 6.10).

Collembola Collembola were most abundant in the forest plots, recorded in low numbers in the jungle rubber and rubber plantation plots and absents from the Paraserianthes plots (Figure 6.11).

Lepidoptera Lepidoptera were notably abundant in only one plot, the BS8 Paraserianthes plots (Figure 6.12).

Acari Acari were more abundant in the secondary forest plot and one of the logged plots (BS4) than elsewhere (Figure 6.13).

Orthoptera Orthoptera were uncommon in all plots, particularly the rubber plantation plots, and were not recorded in the Paraserianthes plots (Figure 6.14).

Blattodea Small numbers of Blattodea were recorded but they were most abundant in the secondary forest plot and one of the jungle rubber plots (BS11) and not recorded in the Paraserianthes plots (Figure 6.15).

74 Neuroptera Very few Neuroptera were recorded in the survey, none at all in the jungle rubber and Paraserianthes plots (Figure 6.16).

6.5.2 Butterfly transects:

6.5.2.1 Total butterflies:

The total number of butterflies caught or seen in an hour ranged from almost 50 in one of the rubber plantation plots to less than one in the Imperata grassland plots (Figure 6.17). Butterflies were particularly uncommon in the Imperata and Cassava plots and in one of intact forest plots (BS2), and most abundant in the jungle rubber, Chromolaena, one of the logged forest plots (BS4) and one of the rubber plantation plots (BS9). Figure 6.18 shows the numbers of butterflies seen; that is, it excludes the relatively small numbers of butterflies caught. It is included to show the variation between different sampling periods. The number of butterflies recorded in each family is described below.

Papilionidae The greatest numbers of papilionids were recorded in the jungle rubber plots and one of the rubber plantation plots (BS9), and none were recorded in the Cassava and Imperata plots (Figure 6.19).

Pieridae Large numbers of pierids were recorded in one of the rubber plantation plots (BS9), intermediate numbers were recorded in the jungle rubber plots and one of the logged forest plots (BS4), and few or none were recorded elsewhere (Figure 6.20).

Nymphalidae Nyphalids were more abundant in the jungle rubber plots and the Chromolaena plot than elsewhere (Figure 6.21).

Lycaenidae Lycaenids were more abundant in the jungle rubber, rubber plantation and one of the logged forest sites than elsewhere and notably absent from the Cassava, Imperata and one of the intact forest plots (BS2) (Figure 6.22).

6.6 Discussion:

6.6.1 Preliminary results: 6.6.1.1 Canopy fogging:

It must be emphasised that the above results are preliminary, subject to amendment after the final order sorting and include no species diversity information. The following tentative observations should, however be highlighted as a basis for subsequent discussion.

• Eleven plots were surveyed, producing a total of 22,700 arthropods, an average of 97m-2. • The most abundant groups were ants and termites. • The abundance and composition of arthropod taxa was affected by land use as follows: • total arthropod abundance and the abundance of ants, Coleoptera, spiders, Hemiptera, Thysanoptera, Hymenoptera (other than ants) and Blattodea was greatest in the jungle

75 rubber plots; however, these plots contained relatively low numbers of Collembola, Acari and Neuroptera; • the abundance of arthropods in the intact forest plots was surprisingly low but these plots, like all ‘forest’ plots, contained relatively high numbers of Collembola; • arthropod numbers in the logged and secondary plots were greater than or similar to those found in the intact forest plots; • total arthropod numbers were lowest in the plantation plots; the rubber plantation plots had particularly low numbers of ants, Collembola, Orthoptera and Blattodea and the Paraserianthes plots contained no Collembola, Orthoptera, Blattodea and Neuroptera.

In discussing these results, their unique nature must be borne in mind: no other study of the effects of land use on arthropod diversity has included such a range of land uses. Thus we can compare the results of this study with surveys of intact and disturbed forest elsewhere, but we cannot compare the jungle rubber and plantation plots with studies elsewhere, because this is the first time this has been attempted. Nevertheless:

• The mean total number of arthropods recorded in this survey is in line with that recorded elsewhere (e.g. Watt et al. (1997a) in Cameroon). • The numbers of ants and other taxa accord with other studies, but the number of termites is markedly higher than found elsewhere. • This study is similar to a few others (e.g. Eggleton et al. 1996, Watt et al. 1997ab, Lawton et al. 1998) in finding that the replacement of intact forest with other land uses tends to result in a decrease in the abundance of several groups of arthropods. • This survey suggests that some land-use alternatives to intact forest, such as jungle rubber, may be rich in arthropods.

Differences in arthropod abundance do not necessarily lead to parallel differences in arthropod diversity so it must again be emphasised that these comments are tentative.

6.6.1.2 Butterfly survey:

The butterfly survey suffered from being too rapid. Because of the priority given to canopy sampling in as many plots as possible, we were only able to spend a maximum of two person hours in each plot. This meant that the numbers of butterflies caught were too small to permit analysis, and we have relied instead on the numbers seen during the transect walks. Even these data are not as useful as they would have been if we had been able to spend about twice as long in each plot. There is also the concern that the numbers of butterflies were particularly low because of the recent drought.

It would, therefore, be wrong to conclude too much from this survey. However the following tentative conclusions may be made:

• Butterfly abundance in most families was notably high in the jungle rubber plots. • The total number of butterflies in one of the rubber plantation plots was higher than elsewhere, mainly due to the particularly high number of pierids recorded there. • Numbers of butterflies in one the intact forest plots was particularly low (lower than in any of the other forest or plantation plots).

76 • The number of butterflies, particularly nymphalids, in the Chromolaena plot was surprisingly high. However, the proximity of this very small plot to the jungle rubber plots should be noted - these plots also had similar numbers of nymphalids. • Very small numbers of butterflies were recorded in the Cassava and Imperata plots.

6.6.2 Review of methods: 6.6.2.1 Canopy fogging:

Eleven sites were fogged and the material collected partially sorted in ten days (excluding time spent travelling and organising). Excluding sorting time (travel etc.), each plot took six whole days (or approximately 30 person days, comprising twelve person days of the consultant’s time, six person days from technical support staff and twelve person days from labourers). A total of about ten person days were spent partially sorting arthropods during and immediately after the survey. It is critically important that fogging surveys produce comparable data. This can only be guaranteed by the selection of representative plots and complete coverage of the canopy of each plot by the insecticide fog. We consider that all the plots chosen were representative of the land-use types in the survey area. We also consider that some plot types were more affected than others by the recent exceptional dry season. In particular, the intact forest plots and the Paraserianthes plots had much less canopy foliage than the rubber (jungle and plantation) plots.

Most of the plot ‘pairs’ within each land use provided adequate replicates. However, the jungle rubber plots and the rubber plantation plots may have been too close to be considered true replicates for arthropod survey. The finding that there was marked variability in the abundance of arthropods in these plots demonstrates how spatially variable arthropod communities are in forests and plantations.

6.6.2.2 Butterfly survey:

As mentioned above, insufficient time was given to the butterfly survey because of other priorities. Two (person) hours were spent collecting and recording the numbers of butterflies present in each plot. Surprisingly, this led to relatively little variation within the numbers of butterflies seen - note the errors in Figure 6.18. A relatively small increase in the amount of time spent recording butterfly abundance would have yielded much more useful data. Considerably more time would have been needed to collect useful data on species composition. The recent drought is likely to have reduced the number of butterflies in the area and this factor, plus the small amount of time spent surveying butterflies, means that we have probably considerably underestimated the abundance and diversity of butterflies in the study area.

The comments above regarding replication apply to the butterfly survey as well. For example, many individual butterflies were seen flying from one plot to another in the rubber plantation. Plot size is likely to have affected the results in at least one case: the surprisingly numbers of butterflies recorded in the relatively small Chromolaena plot may have been dispersing from the nearby jungle rubber plots.

6.6.3 Relevance of study at regional and global levels:

As mentioned above, no previous study has investigated the effects of such a range of land uses on arthropod diversity. It is therefore unique regionally and one of a very few similar studies globally (Eggleton et al., 1996; Watt et al., 1997; Lawton et al,. 1998).

6.6.4 Relevance to Rapid Biodiversity Assessment:

This survey is relevant to Rapid Biodiverity Assessment (RBA) first, because arthropods comprise the largest component of terrestrial biodiversity. Second, the techniques employed fulfilled the criterion of 'rapid' because of the short time spent collecting samples in the field. Third, both methods were designed to produce comparable results.

6.6.5 Need for further surveys in this and other regions:

Generally, many more surveys such as this are needed to assess the impact of land use change on biodiversity. These surveys should include as many land-use types as possible. For example, a similar survey in many other parts of Sumatra should include oil palm (not yet widely planted in the part of Jambi where this survey was conducted). Specifically, surveys such as this should be repeated where there is significant seasonal variation in the abundance of the taxa being considered or where particular conditions prevail. It is likely, for example, that this survey was affected by the severe drought which preceded it.

6.7 Conclusions:

Tentative conclusions are presented above at the start of the Discussion section. The main conclusions are summarised below: • This study is similar to a few others in finding that the replacement of intact forest with other land uses tends to result in a decrease in the abundance of several groups of canopy arthropods. • However, this survey suggests that some land use alternatives to intact forest, such as jungle rubber, may be rich in canopy arthropods. • The butterfly survey was of limited value apart from demonstrating that arthropod abundance and diversity in Cassava and Imperata was considerably poorer than in all other land use types.

6.8 Recommendations:

It is recommended that:

• Further studies such as this are carried out to assess the impact of land use change on biodiversity. • Such studies should use RBA techniques because it is more important to survey many land-use types adequately than a few sites in unnecessary detail. • More research is therefore needed to establish suitable RBA techniques, particularly standard techniques for particular taxa. • RBA projects should follow the multi-taxa approach adopted here (Gillison et al. 1998), so that as much biodiversity as possible is sampled without any assumptions being made about 'indicator' taxa, and so that relationships between the diversity of different taxa can be better understood and, perhaps, lead to the development of reliable biodiversity indicators.

78 6.9 References1

Colwell, R. K., and J. A. Coddington. (1994). Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society of London Series B- Biological Sciences 345:101-118. Eggleton, P., D. E. Bignell, W. A. Sands, N. A. Mawdsley, J. H. Lawton, and N. C. Bignell. (1996). The diversity, abundance and biomass of termites under differing levels of disturbance in the Mbalmayo Forest Reserve, southern Cameroon. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 351:51-68. Gillison, A.N. (1988). A Plant Functional Attribute Proforma for Dynamic Vegetation Studies and Natural Resource Surveys. Tech. Mem. 88/3, CSIRO Div. Water Resources, Canberra. Hill, J. K., K. C. Hamer, L. A. Lace, and W. M. T. Banham. (1995). Effects of selective logging on tropical forest butterflies on Buru, Indonesia. Journal Applied Ecology 32:754-760. Krebs, C. J. (1989). Ecological Methodology, 1st Edition. Harper Collins, New York. Lawton, J. H., D. E. Bignell, B. Bolton, G. F. Bloemers, P. Eggleton, P. M. Hammond, M. Hodda, R. D. Holt, T. B. Larsen, N. A. Mawdsley, N. E. Stork, D. S. Srivastava, and A. D. Watt. (1998). Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest. Nature 391:72-76. Pollard, E., and T. J. Yates. (1993). Monitoring Butterflies for Ecology and Conservation. Chapman and Hall, London. Pollard, E., D. Moss, and T. J. Yates. (1995). Population trends of common british butterflies at monitored sites. Journal Applied Ecology 32:9-16. Stork, N., J. Adis, and R. K. Didham, editors. (1995). Canopy Arthropods. Chapman and Hall, London. Watt, A. D., N. E. Stork, P. Eggleton, D. Srivastava, B. Bolton, T. B. Larsen, and M. J. D. Brendell. 1997a. Impact of forest loss and regeneration on insect abundance and diversity. Pp. 274-286 in A. D. Watt, N. E. Stork and M. D. Hunter, (eds.) Forests and Insects. Chapman and Hall, London. Watt, A. D., N. E. Stork, C. McBeath, and G. L. Lawson. 1997b. Impact of forest management on insect abundance and damage in a lowland tropical forest in southern Cameroon. Journal Applied Ecology 34:985-998.

1References to other reports in this series to be inserted.

79 350

300 Total arthropods

250

e 200

r e

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180 Ants

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140

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120

r 100

e b

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0 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac d d r r e n n e e t t ge ge da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa Figure 1 (above) and 2 (below): mean abundance of total arthropods and ants, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.1. & 6.2

80 1000

Termites

2 100

m u

N 10

1 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac d d r r e n n e e t t ge ge da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa 35

30 Coleoptera

e 20

r e

b 15

u N 10

Figure 3 (above) and 4 (below): mean abundance of termites and Coleoptera, respectively, assessed by canopy fogging in the Pasir Mayang ar Jambi, Sumatra, November 1997.

Figures 6.3 & 6.4

81 16

14 Diptera

12 2

m 10

r 8

b m

u 6 N

S1 S2 5 4 3 10 11 S8 S9 6 7 B B S S BS S S B B BS BS t, t, , B , B , B B ., ., , , c c d d ry r, r nt nt s s ta ta e e a e be la la he he In In gg gg nd bb b p p nt nt o o o u ru r r ia ia L L c r e be be r r Se le gl b b se se ng n u u ra ra u Ju R R a a 14 J P P

12 Hemiptera

e 8

r e

b 6

u N 4

Figure 5 (above) and 6 (below): mean abundance of Diptera and Hemiptera, respectively, assessed by canopy fogging in the Pasir Mayang are Jambi, Sumatra, November 1997.

Figures 6.5 & 6.6

82 25

Thysanoptera

2 m

e b

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14 Spiders

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m 10

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0 1 2 3 0 1 8 9 6 7 e S S S5 S4 S 1 1 S S S S g B B B B B S S B B B B ra t, t, , , y, , B r B t., t., s, s, e ac ac ed ed r r e n n e e v t t g g da be b la la th th A In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa

Figure 7 (above) and 8 (below): mean abundance of Thysanoptera and spiders, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.7 & 6.8

83 14

12 Psocoptera

e 8

r e

b 6

u N 4

10 Hymenoptera 9

2 m

r 6

e b

m 5

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1 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac ed ed r r e n n e e t t g g da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa Figure 9 (above) and 10 (below): mean abundance of Psocoptera and Hymenoptera, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.9 & 6.10

84 10

9 Collembola 8

2 m

r 5

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6 Lepidoptera

e 4

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0 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac d d r r e n n e e t t ge ge da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa Figure 11 (above) and 12 (below): mean abundance of Collembola and Lepidoptera, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.11 & 6.12

85 4

3.5 Acari

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0 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac d d r r e n n e e t t ge ge da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa Figure 13 (above) and 14 (below): mean abundance of Acari and Orthoptera, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.13 & 6.14

86 3.5

3 Blattodea

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Neuroptera

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0 1 2 3 0 1 8 9 6 7 S S S5 S4 S 1 1 S S S S B B B B B S S B B B B t, t, , , y, , B r B t., t., s, s, ac ac ed ed r r e n n e e t t g g da be b la la th th In In g g n b ub p p n n Lo Lo co ru r er er ria ria e le le b b e e S g g ub ub s s n un R R ra ra Ju J Pa Pa Figure 15 (above) and 16 (below): mean abundance of Blatodea and Neuroptera, respectively, assessed by canopy fogging in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.15 & 6.17

87 50

45 Total butterflies 40

u o

h 30

e p

r e

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1 2 5 4 3 10 11 8 9 6 7 6 4 5 2 3 BS BS S S BS S S BS BS BS BS S1 S1 S1 S1 S1 t, t, , B , B , , B B t., t., , , B B B B B ac ac ed ed ry er er n n es es a, a, a, a, a, nt nt g g da b bb la la th th en v v at at I I og og on ub ru r p r p an an la sa sa pir pir L L c r e be be ri ri o s as m m Se gle gl b b se se om Ca C I I n un Ru Ru ra ra r Ju J Pa Pa Ch 50

45 Total butterflies seen 40

u o

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S1 S2 5 4 3 10 11 S8 S9 6 7 16 4 5 12 13 B B S S BS S S B B BS BS S S1 S1 S S t, t, , B , B , , B B t., t., , , B B B B B ac ac ed ed ry er er n n es es a, a, a, a, a, nt nt g g da b bb la la th th en v v at at I I og og on ub ru r p r p an an la sa sa pir pir L L c r e be be ri ri o s s m m Se gle gl b b se se om Ca Ca I I n un Ru Ru ra ra r Ju J Pa Pa Ch

Figure 17 (above) and 18 (below): mean abundance of butterflies seen and caught and seen only, respectively, assessed by transect counts in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.17 & 6.18

88 6

Papilionids

5 r

u 4

e p

b m

u 2 N

S1 S2 5 4 3 10 11 S8 S9 6 7 16 4 5 12 13 B B BS BS BS S S B B BS BS S S1 S1 S S t, t, , , , , B B t., t., , , B B B B B ac ac ed ed ry er er n n es es a, a, a, a, a, nt nt g g da b bb la la th th en v v at at I I og og on ub ru r p r p an an la sa sa pir pir L L c r e be be ri ri o as as m m Se gle gl b b se se om C C I I n un Ru Ru ra ra hr Ju J Pa Pa C 35

30 Pierids

r 20

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m u N 10

S1 S2 5 4 3 10 11 S8 S9 6 7 16 4 5 12 13 B B S S BS S S B B BS BS S S1 S1 S S t, t, , B , B , , B B t., t., , , B B B B B ac ac ed ed ry er er n n es es a, a, a, a, a, nt nt g g da b bb la la th th en v v at at I I og og on ub ru r p r p an an la sa sa pir pir L L c r e be be ri ri o as as m m Se gle gl b b se se om C C I I n un Ru Ru ra ra hr Ju J Pa Pa C

Figure 19 (above) and 20 (below): mean abundance of papilionids and pierids, respectively, assessed by transect counts in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.19 & 6.20

89 25

Nymphalids

u o

h 15

r e

b 10

u N

5 Lycaenids

r r r r

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o o o o

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p p p p

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b b b b

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N N N N

Figure 19 (above) and 20 (below): mean abundance of nymphalids and lycaenids, respectively, assessed by transect counts in the Pasir Mayang area, Jambi, Sumatra, November 1997.

Figures 6.21 & 6.22

SECTION 7: SOIL MACROFAUNA

GROUND-DWELLING ANTS, TERMITES, OTHER MACROARTHROPODS AND EARTHWORMS.

by D.E. Bignell1, E. Widodo1, F.X. Susilo2 and H. Suryo3 1Tropical Biology & Conservation Unit, University Malaysia Sabah, Kota Kinabalu, 2Lampung University Faculty of Agriculture, Bandar Lampung, 3Gadjah Mada University Faculty of Forestry, Yogyakarta

7.1 Introduction:

The Humid Forest Zones (HFZ) of the tropics cover about 8% of the Earth's land surface, of which about 20% occurs in SE Asia. The present forest is a mosaic of different types of land use: patches of logged-over forest in varying states of regrowth, secondary forest and fallow vegetation, some tree plantations including forms of agroforestry and significant remnants of primary vegetation, as well as degraded grasslands exhausted of almost all arable potential (Swift & Mutsaers, 1992; van Noordwijk et al., 1997). The dominant soils are acidic (oxisols and ultisols derived from low activity clays), commonly exhibiting Al toxicity, low cation- exchange capacity, low base saturation and low P availability. Consequently, they have low inherent fertility and, in many cases, low structural stability if soil organic matter is excessively depleted. The traditional food-production systems of the HFZ are those of shifting cultivation (slash and burn) and (increasingly) recurrent fallow rotation, with rice, plantain, cocoyam (Taro), maize, Cassava and groundnut as typical staples, the last two of these having relatively low fertility requirements. In recent years, socioeconomic factors (including changing world prices for cocoa, oil-palm, rubber latex and other cash crops), population growth, the restriction of urban employment opportunities and legal uncertainties over title to timber revenue, have led to an increase in the clearance of forest for food or cash-crop production and, concomitantly, an accelerated decline in the fertility of soils under cultivation as fallow periods have shortened (Woomer & Swift, 1994). Agricultural research for the HFZ has therefore been directed towards the improvement of sustainability, for example, by the conservation of soil organic matter and the provision of better mulching regimes. Added to this is the development of flexible mixed cropping systems, for example, the combination of marketable tree crops with field-planted staples, or mixtures of commercially valuable trees such as rubber and natural secondary regrowth (Scholes et al., 1994; van Noordwijk, 1997). Multistrata systems provide an opportunity for the simultaneous production of timber (and/or other tree crops) and food, with a sustained supply of organic matter and nutrients to soil and the stabilization of structure.

The importance of macrofauna to the promotion of tropical soil fertility has been stressed in recent reviews (Fragoso et al.,1993; Lavelle et al.,1997; Garnier-Sillam & Harry, 1995; Nash and Whitford, 1995; Brussaard & Jumas,1996; Wood, 1996). The distribution, protection and stabilization of organic matter, the genesis of soil structure (macroaggregates), humification, the release of immobilized N and P, the improvement of drainage and aeration, and the increase in exchangeable cations have all been demonstrated in soils modified by termites and earthworms (e.g. Mulongoy & Bedoret, 1989; Lavelle et al.,1992; 1998). Soil ants and other macrofauna represent predators, herbivores (granivores) and bioturbators, bringing about important changes in the physical and chemical properties of soils, as well as dispersing plant propagules. Networks of galleries and chambers increase the porosity of the soil, increasing

91 drainage and aeration (Cherrett, 1989) and reducing bulk density (Baxter and Hole, 1967). Ant- plant communities are much more species-rich in the tropics than elsewhere; a pattern associated with habitat heterogeneity (Davidson and McKey, 1993; Folgarait, 1996).

Depletion of termite abundance and diversity is now a well-established effect of forest clearance (Wood et al., 1982; Eggleton et al., 1995; 1996). Effects on earthworms also include the loss of typical forest species, but also possible invasion by exotic species, with adverse consequences for soil structure (Reddy & Dutta, 1984; Barros et al., 1996). Information on ants is limited, but Belshaw and Bolton (1994) found similar levels of leaf litter ant diversity in secondary forest, primary forest and cocoa plantations in Ghana. A more recent study in Cameroon by Watt et al. (1997), showed that moderate forest disturbance, for example, by enrichment planting after partial clearance, increased species numbers and overall ant abundance in both leaf-litter and canopy-dwelling ants. Complete clearance reduced abundance severely, although diversity was comparable to that in closed canopy forest. There is a general consensus that the conservation of indigenous invertebrate biodiversity should be an integral part of land-management strategies in the HFZ, if the goal of increased crop-yield sustainability (and concomitant forest conservation) is to be realized (e.g. Smith et al., 1993; Lavelle, 1996; Lavelle et al., 1998).

The soil biota (and hence soils as a whole) respond to human-induced disturbance such as agricultural practices, deforestation, pollution and global environmental change with many negative consequences including loss of primary productivity, loss of cleansing potential for wastes and pollutants, disruption of global elemental cycles, feedbacks on greenhouse gas fluxes and erosion. At the same time, global food supply depends on intensive agriculture. As intensification proceeds, above-ground biodiversity is reduced, one consequence of which is that the biological regulation of soil processes is altered and often substituted by the use of mechanical tillage, chemical fertilizers and pesticides. This is assumed to reduce below-ground diversity as well, which, if accompanied by the extinction of species, may cause losses of function and reduce the ability of agricultural systems to withstand unexpected periods of stress and bring about undesirable effects. Scientists have begun to quantify the causal relationship between i) the composition, diversity and abundance of soil organisms, ii) sustained soil fertility, and iii) environmental effects such as greenhouse gas emission and soil carbon sequestration.

Large numbers of farmers in the tropics have limited access to soil inputs (i.e. fertilizer and pesticides) but are nonetheless forced by circumstances to drastically reduce the complexity of their agroecosystems in an attempt to intensify production. An alternative solution is to intensify while at the same time retaining a greater degree of above-ground diversity. The maintenance of diversity of crops and other plants in cropping systems is widely accepted as a management practice which buffers farmers against short-term risk. Enhanced biodiversity and complexity above-ground contributes to the re-establishment or protection of the multiplicity of organisms below-ground able to carry out essential biological functions. This can be considered at both the field and the landscape level to enhance structural complexity and functional diversity, especially in degraded lands.

In this paper, we report quantitative and qualitative sampling in 7 representative land uses in or close to the Pasir-Mayang Forest Reserve, Jambi Province, central Sumatra, using rapid assessment methods which enabled most sites to be examined in 1-2 days. Ants and other macrofauna were sampled qualitatively by the use of pitfall traps and quantitatively from standard soil monoliths.

7.2 Sites:

The sites selected were chosen to have an approximately even spacing along a presumed disturbance gradient from pristine forest through to degraded grassland (Table 7.1). As a total of only 16 sites was available, not all land uses could be addressed, and no replication was attempted other than the within-site pseudo-replication inherent in transects and pitfall lines (see below). Sites were generally sampled last, in a planned sequence, after botanical, ornithological, soil and other site surveys had been completed and following the sampling of mammals, canopy arthropods and butterflies. Sampling was usually completed in 2 days, including dissection of monoliths. An intuitive grading of the sites, based on expected macrofaunal diversity, would be:

BS1 ---> BS3 ---> BS10 ---> BS8 ---> BS6 ---> BS14 ---> BS12 least disturbed most disturbed most diverse least diverse

The notional gradient used, reflecting disturbance history, disturbance intensity and vegetation is: BS1---Æ BS3---Æ BS6---Æ BS8---ÆBS10 ---Æ BS12 ---Æ BS14

Table 7.1. Seven landuses selected for ant and other macrofaunal sampling in Pasir Mayang and adjacent areas of central Sumatra.

Site Dominant vegetation form General character GPS reference coding BS 1 Intact rainforest A small area of pristine lowland 01-04-47 S forest on a moderately steep 102-06-02 E slope, well drained with closed Pasir Mayang stratified canopy and generally light understorey. Tree buttresses and stilts present.

BS 3 Secondary rainforest A ridge-top site contiguous with 01-04-43 S BS1 but logged-over with 102-05-55 E secondary regrowth on old log collection points and skid trails. Pasir Mayang Transects and pitfalls placed to run through secondary areas. Generally patchy canopy but of limited stratification. High liana/creeper burden.

93 Site Dominant vegetation form General character GPS reference coding BS 6 Young 3/4 yrs A heavily disturbed site with line 01-05-59 S Paraserianthes plantation planted Sengon trees established 102-06-43 E after complete clearance. Canopy Pasir Mayang very open and the ground with a heavy load of dead wood.

BS 8 Rubber plantation A mature monospecific 01-05-25 S plantation in current production 102-07-05 E for latex, located on a gentle Pasir Mayang slope upper to ridge top. Canopy closure complete and herb/understorey layers very sparse. Large decaying tree trunks from previous forest clearance present with moderate dead wood load. About 15 yrs. old.

BS 10 Jungle rubber Mixture of old rubber trees still 01-10-12 S in production and secondary 102-06-50 E forest regrowth with high Pancuran Gading liana/creeper burden. About 25- 30 yrs. old, at end of cycle ready for felling. Canopy closure ± complete and well stratified. Flat site, riverine.

BS 12 Imperata cylindrica Large open ridge-top site devoid 01-36-05 S Grassland: "alang-alang" of trees with knee-high uniform 102-21-22 E. stand of course grass. Little or no Kuamang dead wood. Ground cracked and Kuning very hard.

BS 14 Cassava garden Open ridge-top site with line- 01-35-58 S planted Cassava, about 2 yrs old. 102-21-11 E Weeded to prevent growth of Kuamang other vegetation. Ground very Kuning disturbed but little or no dead wood.

An ordination of 16 sites, incorporating 9 land uses, based on 27 plant functional attributes and 3 canopy characters (height, cover and stem basal area) suggests the following sequence:

BS1 ---> BS3/BS10 ---> BS6 ---> BS8 ---> BS12 ---> BS14 most botanically diverse least botanically diverse (source A. Gillison, pers.comm.)

94 An expanded version of Table 1, incorporating botanical, soil physio-chemical data and additional site information is given as Annex III, Table 12.1.

7.3 Aims and objectives:

• To provide data on species richness, numerical density and biomass density for ground- dwelling ants, with estimates of population variance for numerical density and biomass density, in 7 LUTs. • To provide data on numerical density and biomass density of earthworms and termites, with estimates of population variance, in 7 land uses. • To provide an estimate of species richness of earthworms. • To provide an estimate of taxonomic richness (to the best level of resolution possible) for other macrofauna (in addition to earthworms, ants and termites). • To give pooled (i.e. overall) data for numerical density and biomass density for other macrofauna. • To allocate basic functional attributes to macrofauna.

The objectives were developed to test the following hypotheses:

• Agricultural intensification results in a reduction of soil biodiversity, leading to a loss of ecosystem services detrimental to sustained productivity. • Above-ground and below-ground biodiversity are interdependent across scales of resolution from individual plant communities to the landscape. • Agricultural diversification (at several scales) promotes soil biodiversity and enhances sustained productivity. • Sustainable agricultural production in tropical forest margins is significantly improved by enhancement of soil biodiversity.

7.4 Methods:

7.4.1 Review of existing methods:

General approaches to the sampling of invertebrate animals, and the advantages and disadvantages of particular methods, are descibed by Murphy (1962), Phillipson (1971) and Southwood (1978).

Soils differ greatly in composition, particle size, structure, depth and compaction, and whether they are under trees, grassland or cultivation. Since the soil fauna is incorporated closely into the soil structure, the assessment of populations of these organisms is extremely difficult and laborious, and generally necessitates a wide range of specialized techniques if animals in the three major size categories (macrofauna, mesofauna and microfauna) are to be assessed (Edwards, 1991).

The basic options are a) hand soil sifting and sorting (including litter layer dissection), b) trapping with or without baits, and c) extraction methods. In the last category are techniques based on flotation, which separates buoyant animals from the inert soil particles with water- based solutions (for example brine or sugar solutions) or organic solvents, or enables them to escape from the particle matrix by swimming (for example enchytraeids and nematodes in wet funnel methods), or dry heat extraction in which litter or soil samples suspended above funnels

95 are slowly dried, causing animals to migrate out of the litter into the funnels, from which they can be recovered, preserved and concentrated in alcohol (Bater, 1996). For ants, which are exceptionally mobile and respond rapidly to desiccation, a special modification of the extraction principle can be employed through the use of Winkler bags. These are narrow-mesh closed fabric bags forming a double-pyramid shape and enclosing suspended samples of soil or litter; the bags are hung up in a dry place for 6-8 days while the samples dry out naturally and any ants they contain are eventually captured in pots of alcohol fitted into the lower apex.

However, extraction methods like this are generally slow and usually require some kind of laboratory base, so for rapid assessment focussed on the larger soil animals, it is normally sufficient to use just hand sorting; i.e. a measured quantity of soil or litter (usually delimited by a quadrat) is gradually crumbled over a sheet of plastic or other material and the invertebrates collected with forceps or pooter as they are released and stored in a suitable preservative (5% formalin for earthworms and gastropods; 70% alcohol for other invertebrates). Samples tend to accumulate faster than they can be sorted, so it is permissible to store samples in plastic bags (but out of direct sunlight) for up to 12 hours for later sorting. The efficiency of hand-sorting is generally high for animals which can be seen with the naked eye, as long as field assistants are adequately trained, but some authors have reported making allowances of up to 12% for lost or undiscovered specimens (e.g. Wood et al., 1982).

Trapping methods can be used to exploit accidental encounter by invertebrates, but baiting is not usually employed for ants, as attractants may introduce bias by selecting for some species more than others. Pitfall traps, containers sunk into the soil flush with the soil surface, containing either a preservative or some other immobilizing fluid and with raised covers to prevent flooding by rain, are probably the most commonly used method of catching invertebrates (Bater, 1996). The main variations are in the size of container and the use, or otherwise, of guiding fins or other corralling devices to increase interception. The limitations of pitfall traps are largely in the interpretation of data, since the numbers of animals trapped are related both to overall numbers present and their activity, and so may not sample each population entirely. There is a tendency for such traps to accumulate ants, beetles, crickets, isopods, myriapods and spiders (all of which are active on the surface of the ground, particularly at night). The optimum period for capture is about 24 hours, after which traps are often disturbed by vertebtrates and birds. There are methods available to convert the numbers of invertebrates trapped to populations, usually based on physically delimiting sampling areas with some form of barrier or using mark-recapture techniques.

7.4.2 Functional classification of soil fauna: (after Lavelle, 1988; Anderson and Ingram, 1993).

Soil invertebrates can be classified according to their feeding habits and distribution in the soil profile as follows:

Epigeic species which live and feed on the soil surface. These may act as litter transformers or the predators of litter transformers, but do not actively redistribute plant material.

Anecic species which remove litter from the soil surface through their feeding, redistributing it to other horizons or locations, accompanied by effects on soil structure and hydraulic properties.

96 Endogeic species which live entirely within the soil, feeding on organic matter and dead root materials, which are mixed with other components of the soil, creating mineral-humus complexes and influencing a large suite of soil properties. The quantification of these effects on soil processes requires detailed study, but a simple characterization of macrofauna can assist in assessing their role in different landuses and under various regimes of management (Table 7.2).

Table 7.2 Functional classification of common soil fauna

Taxon Category Ants Epigeic and anecic Arachnids (esp. spiders) Epigeic Beetle adults Epigeic and endogeic Beetle larvae Epigeic Cockroaches Epigeic Centipedes Epigeic Cicada larvae Endogeic Crickets Epigeic Earthworms (pigmented) Epigeic and anecic Earthworms (unpigmented) Endogeic Millipedes Epigeic Slugs and snails Epigeic Wood-feeding termites Epigeic and anecic Soil-feeding termites Endogeic Fungus-growing termites Anecic Woodlice Epigeic

7.4.3 Sampling design:

Sampling in each land use is based on a single quadrat of 40x5 m, which is compatible with concurrent botanical and other pedological sampling exercises (Gillison and Liswanti, this volume). The recommendation is for a minimum of 5 soil monoliths, each 25x25x30cm spaced along the mid-line of the transect at approximately 8m intervals, accompanied by at least 10 pitfalls (using 14cm diameter glass or plastic containers) arranged in a flanking line parallel to the transect or along its long edge. The choice of the starting point for the transect should be random, but its direction is normally determined by the line of best visual habitat homogeneity.

7.4.4 Procedure:

Procedures follow Anderson and Ingram (1993) closely:

a. 5 sampling points (for monoliths) are located and marked within the transect.

b. 10 pitfall traps are fitted at roughly 4m intervals along one flank of the transect. The traps are put in during the afternoon or early evening and emptied 24 hours later. Each trap contains a little water, with a few drops of detergent added, to immobilize specimens by drowning.

97 c. At each sampling point litter is removed from within a 25cm quadrat and hand- sorted at the site.

d. Isolate the monolith by cutting down with a spade a few centimetres outside the quadrat and then digging a 20cm wide and 30cm deep trench around it. NB. In a variant of the method not adopted in Pasir Mayang, all invertebrates longer than 10cm excavated from the trench are collected; these will be mainly large millipedes and earthworms with very low population densities but representing an important biomass. Their abundance and biomass can be calulated on the basis of 0.42 m2 samples, i.e. the width of the block plus two trench widths, squared.

e. Divide the delimited monolith block into three layers, 0-10cm, 10-20cm and 20- 30cm. This can be done conveniently using a parang or machete held horizontally and grasped at both ends. Hand-sort each layer separately. If time is short or the light poor (sorting in closed canopy forest is usually difficult after about 3.30pm), bag the soil and remove to a laboratory. Ants can be extracted by gently brushing small (handful) quantities of soil through a course (5mm) sieve into a tray: the sieve retains the ants.

f. Record the number and fresh (preserved, after blotting) weight of all animals and identify to at least the taxonomic and functional levels indicated in Table 7.2 (but preferably further). The presence and weight of termite fungus combs (if any) should also be noted.

7.4.5 Analysis:

The following steps should be followed: i) Make a list of species, if possible grouped into subfamilies or families. Use generic names to generate alphabetical orders. Use the results from pitfall traps and monoliths to compile this list.

Fully identified species should be listed with the full binomial and descriptive authority: e.g. Dorylus laevigatus Smith

Morphospecies should be listed by number: e.g. Crematogaster sp. 1 Crematogaster sp. 2 ...... etc.

Species identified only to genus should be listed without numbers: e.g. Colobobsis sp.

Incorporate the species list into a table showing the sites where each occurred. ii) Estimate abundance as numbers m-2 from each monolith (multiply the raw number per monolith by 16 (except earthworms and millipedes, see above), combining data for all species.

98 Calculate an arithmetical mean. To estimate the 95% confidence limits the primary data should be transformed as log10(x+1). If there are not too many zeros, this should roughly normalize the data and produce homogeneous variances from group to group. In difficult cases, a log-log transformation can be tried. Apply descriptive statistics to the transformed dataset, including 95% confidence limits, and back transform to obtain a geometric mean. Quote means for untransformed data, together with the geometric mean and confidence limits for log(x+1) transformed data. The transformed data can be used for histograms and site-to-site comparisons.

Estimate biomass as g m-2 in a similar way. Use fresh weight or the mass of blotted preserved specimen, if possible. Avoid the use of dry weight because of the different oven temperatures used by different scientists and the variable water content of different types of organism. Where insect specimens in a range of sizes are available, an alternative method is to calibrate live biomass against head width in representative specimens covering the whole size range. The weight of unknowns can then be estimated from the curve. For log transformations of data, it is most convenient to work in (mg + 1), then back-transform and express as g. iii) Results should be presented as species/taxa lists, plus the standard histograms as illustrated below:

Figure 7.1. Graphical summaries of biodiversity data – some examples

Numerical density Biomass density

= geometric mean and 95% confidence limits, back-transformed 30 3000

2000 20

1000 10

0 A B C D E

Landuse systems

FUNCTIONAL GROUP DIVERSITY RELATIVE ABUNDANCE ABUNDANCE (for example): (for example):

20 2 100 100 OTHERS EPIGEIC ANTS

10 1 50 EARTHWORMS 50 ANECIC

TERMITES ENDOGEIC 0 0

99 iv) An overall quantitative synthesis of data for macrofauna can be attempted using the following matrix:

Table 7.3 Synthesis matrix for macrofauna

Region Landuse System A = natural B C D E control site

e.g. Pasir Mayang x = 80 x = 67 x = 50 x = 95 x = 57 p = 0.1 p = 0.04 p = 0.11 p = 0.05 % = -16 % = -38 % = +19 % = -29 where, x = average of monoliths p = level of significance for a comparison with the control site by an appropriate statistical test. % = percentage difference between the mean of each landuse and the control site, with an indication (+/-) of the direction of change (increase or decrease).

The control site is selected as the least disturbed local land use; in most cases this would be a tropical forest, preferably primary, or else old growth secondary or disturbed primary forest. Arrangement of sites in rank order to form a disturbance gradient may be somewhat arbitrary, especially if site histories are incompletely known, but disturbance intensity, management intensity and time since the imposition of disturbance are the usual criteria employed.

Matrices can be prepared for the following data: • total numerical density • total biomass density • earthworm numerical density • earthworm biomass density • earthworm species richness • termite numerical density • termite biomass density • termite species richness • ant numerical density • ant biomass density • ant species richness • all macroarthropod numerical density • all macroarthropod biomass density v) A qualitative synthesis can be given by answering the following questions:

• what is the effect of each landuse system on biodiversity? • which groups change the most with disturbance and along the land use gradient? • what is the relationship between the functional group changes and the degree of sustainability of each land use?

100

7.4.6 Data analysis:

Carry out a non-parametric ANOVA (Kruskal-Wallis) on each dataset to see if there is a significant difference across the sites (or treatments). This can be followed by pairwise comparisons between sites using the Mann-Whitney U test. Parametric ANOVA can be performed on log transformed data.

7.5 Results:

7.5.1. Ants:

The total number of subfamilies sampled was 8: • Dorylinae: BS10 only • Formicinae: all sites • Myrmecinae: all sites • Ponerinae: all sites except BS14 • Leptanillidae: BS 8 only • Pseudomyrmicinae: BS 6 and BS 10 only • Cerapachyinae: BS 8 only • Dolichoderinae: BS 6, BS 10, BS 12 and BS 14 only

The species lists for each site are available in Annex III, Table 12.11 . Details of ant numerical density and biomass density by site and by stratum are given in Annex III, Table 12.2 and 12.3 , respectively. The following figures summarize ant diversity and abundance:

101 Figure 7.2. Ant species richness 33

20 18 16 16 15

10 9

BS1 BS3 BS6 BS8 BS10 BS12 BS14

Figure 7.3. Ant abundance and biomass

Numerical density Biomass density

7000 = geometric mean and 95% confidence limits, back-transformed

1000

1.5

1.0

500

0.5

0 BS1 BS3 BS6 BS8 BS10 BS12 BS14 Landuse systems

Abundance and biomass were totalled for each monolith and assessed by the non-parametric one-way Kruskal-Wallis ANOVA, using arithmetic data. Abundance and biomass did not vary significantly across the sites (p>0.05)). One-tailed pairwise comparisons of ant abundance between sites were, however, carried out by the Mann-Whitney test (Table 7.10). These generally showed significant differences between some of the richer sites (BS3, BS6, BS10) and those that were highly disturbed (BS12, BS14).

BS3, BS6 and BS10 all had more ants than either BS12 or BS14 (p<0.025 in all comparisons). BS10 had more ants than BS 8 (p<0.025). All other site comparisons were non-significant (see Table 7.10 and Annex III, Table 12.10).

102 BS3 had a higher biomass of ants than BS8 (p=0.05) and BS12 (p=0.025). BS6 and BS10 also had a higher biomass than BS12 (p<0.025). All other site comparisons were non-significant (see Table 7.11 and Annex III, Table 12.10 ).

Untransformed abundance and biomass data from each soil stratum were summed across all sites. Transformed data were used to compute geometric means and 95% confidence limits, using the average of each monolith (numbered 1-5) across all 7 sites (Table 7.8). ANOVA tests on these data showed no significant differences between strata.

In a further analysis of abundance and biomass, all 140 data points (5 monoliths X 4 strata X 7 sites) were transformed and subjected to a parametric two-way ANOVA in which the variables were site and stratum (Table 7.9). This showed a significant variation across sites (abundance: p<0.025; biomass: p<0.05) and between strata (abundance: p<0.001; biomass: p<0.025). However the interactions between site and stratum were not significant for either abundance or biomass (p>0.5).

Functional group allocation (epigeic, anecic, see Figure 7.11) was made from information in Holldobler and Wilson (1990) and by anecdote (E. Widodo and M. Maryati, personal communications).

All three measures of ant activity (species richness, abundance and biomass) were consistent in showing BS3, BS6 and BS10 as sites of high ant activity. On abundance data BS10 was significantly different from two other sites and on biomass data different from one. In statistical analysis, biomass differences were more weakly supported by pairwise comparisons between sites. BS3, BS6 and BS10 had no zero samples from monoliths.

Table 7.4 Abundance and biomass totals (arithmetic data)

Site Total no individuals Total biomass of ants sampled sampled from monoliths. from monoliths, mg

BS1, primary forest 110 109 BS3, logged over 163 83 BS6, Paraserianthes 172 1583 BS8, rubber 39 32 BS10, jungle rubber 169 268 BS12, alang-alang 25 12 Bs14, Cassava 15 105

Table 7.4 gives some indication of the real quality of the data; a small amount of material from which to extrapolate to the landscape level. Nevertheless, it is instructive in illustrating the way in which this group of insects does not have its highest species richness, abundance or biomass in the primary forest, but in two disturbed sites of somewhat different character.

Trends across the sites relative to the control site (BS1, primary forest) are given in Table 7.5, in standard format. The literature contains few data for comparison. Belshaw and Bolton (1994)

103 give average litter-ant abundance across several woodland sites in Ghana as 117 m-2. Watt et al. (1997) examined a range of forest sites in Cameroon representing a disturbance gradient similar to that observed in Pasir Mayang, but give figures in the range 20-80 m-2. However, the lightly and heavily disturbed sites were towards the lower end of this range, while sites with intermediate disturbance yielded the higher abundances. Stork and Brendell (1993) give 3 g m-2 as the biomass of all non-social insects in the forest system of Seram, Indonesia.

Table 7.5 Trends across the sites relatives to the control sites (BS1)

Landuse System Parameter Natural control BS1 BS6 BS8 BS10 BS12BS14 site = BS1

Numerical density

Arithmetical average of 352 522 550 134 541 80 48 monoliths, nos m-2 p value for comparison 0 ns. ns. ns. ns. ns. ns. with control site (transformed data) % difference of means from 0 +48% +56% -62% +54% -77%-86% control site

Biomass density

Arithmetical average of 0.346 0.285 4.889 0.102 0.857 0.03 0.336 monoliths, nos m-2 p value for comparison 0 ns. ns. ns. ns. ns. ns. with control site (transformed data) % difference of means from 0 -18% +1400 -71% +248 -92% -1% control site % % ns. = not significant (p > 0.05)

7.5.2. Termites:

The species lists for each site are available in Section 8 of this report. Details of termite numerical density and biomass density by site and by stratum are given in Annex III, Table 12.4 and 12.5 , respectively. Figures 7.4 and 7.5 summarize termite diversity and abundance.

104 These generally showed significant differences between the richest site (intact rainforest, BS1) and others. In addition, sites BS3 and BS6 were significantly greater in abundance and biomass than site BS12 (the Imperata grassland; p varies between <0.05 and <0.005). There were no other significant differences between sites. Untransformed abundance and biomass data from each soil stratum were summed across all sites. Transformed data were used to compute geometric means and 95% confidence limits, using the average of each monolith (numbered 1- 5) across all 7 sites (Table 7.8). ANOVA tests on these data showed that vertical biomass distribution varied significantly across the sites (Table 7.9; p<0.05). For both abundance and biomass, all 140 data points (5 monoliths X 4 strata X 7 sites) were transformed and subjected to a parametric two-way ANOVA in which the variables were site and stratum. This showed a significant variation across sites (p<0.001) and between strata (p<0.001). The interactions between site and stratum were significant for abundance (p<0.05) and biomass (p<0.01). Termites generally showed highest numerical and biomass densities in the top 10 cm of the soil.

Functional group allocation (anecic, endogeic, see Figure 7.11) was made from knowledge of termite natural history (see Jones, 1999 in preparation). Generally, species nesting arboreally and feeding on the surface were designated anecic, those building epigeal nests and subterranean nests but feeding on the surface anecic, and those feeding within the soil endogeic, wherever the nesting site. The epigeic category was not recognized for termites.

Overall, termite abundance and biomass were heavily reduced by forest disturbance. This confirms their status as sensitive indicators of forest quality (Eggleton et al., 1995; 1996; Watt et al. 1997). Diversity remained relatively high in two disturbed sites (BS3, logged over forest; BS10, jungle rubber), but this was not matched by a corresponding retention of abundance and biomass.

105 Figure 7.4. Termite species richness

30 30

21 21 20

10 10

2 1

BS1 BS3 BS6 BS8 BS10 BS12 BS14

Figure 7.5. Termite abundance and biomass

Numerical density Biomass density

3000 = geometric mean and 95% confidence limits, back-transformed

1000 8.0

6.0

500 4.0

2.0

0 BS1 BS3 BS6 BS8 BS10 BS12 BS14 Landuse systems

7.5.3. All macroarthropods:

Macroarthropods other than ants and termites were recovered from monolith dissections and pitfall traps. These were predominantly Coleoptera, Diptera, Hemiptera, Dictyoptera Orthoptera, Isopoda, Myriapoda and Arachnida, including many juvenile forms. In most, cases identification was made at class and ordinal level only, more rarely to family, but abundance and biomass were determined as for ants and termites. To make use of the resulting data, they were added to those of ants and termites to make a composite dataset respresenting all macroarthropods. This is summarized in Figs. 7.6 and 7.7 below, with detailed information in Annex III, Table 12.6 and 12.7.

106 Figure 7.6. All macroarthropods taxonomic richness

60 60 52 46 40 40 37

23 20

BS1 BS3 BS6 BS8 BS10 BS12 BS14

Diversity score = ant species + termite species + other groups at ordinal level or above (earthworms not included)

Figure 7.7. All macroarthropods abundance and biomass

6000 Numerical density Biomass density

= geometric mean and 95% confidence limits, back-transformed

2000

1000

0 BS1 BS3 BS6 BS8 BS10 BS12 BS14 Landuse systems

A crude diversity index for macroarthropods was obtained by summing ant and termite species richness, then adding other groups at the level of taxonomic resolution obtained. This shows a more even decline across the gradient, with the exception of BS10 (jungle rubber), which scored a higher diversity than any other site. However, it must be borne in mind that groups with inherent very high diversity, e.g. Coleoptera, Diptera, Arachnida, are almost certainly inaccurately represented (i.e. underestimated) by this method.

Abundance and biomass were totalled for each monolith and assessed by the non-parametric one-way Kruskal-Wallis ANOVA, using arithmetic data. Abundance and biomass were found to vary significantly across the sites (Tables 7.9; abundance, p<0.005; biomass, p<0.025). One- tailed pairwise comparisons of ant abundance between sites were carried out by the Mann- Whitney test (Tables 7.10 and 7.11). For abundance, these generally showed significant differences between the richest site (intact rainforest, BS1) and others. In addition, sites BS6, BS8 and BS10 were significantly greater in abundance and biomass than heavily degraded sites

107 (BS12 and BS14; p varies <0.025 to <0.005,except BS6 vs BS14, which is not significant). For biomass, BS1 was significantly greater than BS3, BS12 and BS14. Biomass in BS12 and BS14 was also exceeded by that in BS10 and that in BS12 by BS6 and BS8. Untransformed abundance and biomass data from each soil stratum were summed across all sites. Transformed data were used to compute geometric means and 95% confidence limit,s using the average of each monolith (numbered 1-5) across all 7 sites (Table 7.8). ANOVA tests on these data showed that vertical abundance and biomass distribution varied significantly across the sites (Table 7.9; abundance, p<0.01; biomass, p<0.005). For both abundance and biomass, all 140 data points (5 monoliths X 4 strata X 7 sites) were transformed and subjected to a parametric two-way ANOVA in which the variables were site and stratum. This showed a significant variation across sites (p<0.001) and between strata (p<0.001). The interactions between site and stratum were not significant. The data are, of course, heavily influenced by ant and termite distributions, but for macroarthropods as a whole, the highest numerical and biomass densities were found in the top 10 cm of the soil.

Functional group allocations for macroarthropods other than ants and termites (engineers, litter transformers, macropredators, see Figure 7.10 and epigeic, anecic, endogeic, see Figure 7.11) were made from knowledge of natural history and by reference to Anderson and Ingram (1993; Table 7.2). Animals which did not fit any of these categories, for example sap-feeders, were excluded from the subsequent analysis of functional group distribution across sites.

Few, if any, published studies of soil macroarthropod communities are available for comparison with these data. The inclusion of data for arthropods other than ants and termites appears useful in providing some additional resolution between sites, not obtained from scrutiny of the ants and termites alone. This may be a consequence of the well-known patchiness of soil animal distributions, since individual groups may show very large variance between replicate samples in a single site,but a composite category of macroarthropods may show less overall variation. The category “macroarthropods”, however, disguises group to group turnovers which may characterize disturbance gradients.

7.5.4. Earthworms:

Earthworms were recovered from monolith dissections, but were only rarely present in pitfall traps. Summary data on diversity, abundance and biomass are given in Figs. 7.8 and 7.9, with further details in Appendices 7.8 and 7.9. Overall earthworm diversity was low, with only a single morphospecies recognized from the forested BS1 and BS3 sites. However, very high abundance and biomass were associated with sites BS6 and BS10.

Abundance and biomass were totalled for each monolith and assessed by the non-parametric one-way Kruskal-Wallis ANOVA, using arithmetic data. Abundance and biomass were found to vary significantly across the sites (Tables 7.9; p<0.005). One-tailed pairwise comparisons of ant abundance between sites were carried out by the Mann-Whitney test (Tables 7.10 and 7.11). For abundance, these showed significant differences between the richest site (jungle rubber, BS10; p<0.005) and all others. In addition, site BS6 was significantly greater than BS1, BS3, BS8 and BS12. BS14 (Cassava garden) was relatively abundant, exceeding BS1 and BS3 (p<0.025). For biomass, BS10 exceeded BS8 and BS12 (p variable <0.05 to <0.005), while BS6 exceeded BS1, BS3, BS8, BS12 and BS14. (p variable <0.05 to <0.005). This somewhat anomalous result reflects the large confidence interval associated with site BS10.

108 Untransformed abundance and biomass data from each soil stratum were summed across all sites. Transformed data were used to compute geometric means and 95% confidence limits, using the average of each monolith (numbered 1-5) across all 7 sites (Table 7.8). ANOVA tests on these data showed that vertical abundance and biomass distribution varied significantly across the sites (Table 7.9; p<0.001). For both abundance and biomass, all 140 data points (5 monoliths X 4 strata X 7 sites) were transformed and subjected to a parametric two-way ANOVA in which the variables were site and stratum. This showed a significant variation across sites (p<0.001.) and between strata (p<.001). The interactions between site and stratum were significant for both abundance (p<0.001) and biomass (p<0.001). Earthworms generally showed highest numerical and biomass demsities in the top 10 cm of the soil, with only a few specimens recovered below this level and none in the litter.

Functional group allocation (epigeic, anecic, endogeic, see Figure 7.11) was made from pigmentation (see Lavelle, 1988 and Lavelle et al., 1997). Anecic and endogeic earthworms were considered engineers.

109 Figure 7.8. Earthworms taxonomic richness

6 5 5

4 4 3

2 2 1 1

BS1 BS3 BS6 BS8 BS10 BS12 BS14

Figure 7.9. Earthworms abundance and biomass

60 Numerical density 500

Biomass density

= geometric mean and 95% confidence limits, back-transformed 400

200 20

0 0 BS1 BS3 BS6 BS8 BS10 BS12 BS14 Landuse systems

Few data exist documenting the responses of tropical forest earthworms to disturbance gradients, but increases in population size and biomass following conversion of natural forest to plantation have been observed (Lavelle et al., 1997). The alternative explanation that disturbed sites have abundant dead wood and litter, thereby supporting increased earthworm populations, is not supported by measurements of the actual resources available at BS6 and BS10 (Annex III, Table 12.1 ).

7.6. Synthesis:

Tables 7.6 and 7.7 summarize soil faunal abundance and biomass (respectively) across the 7 sites. Arithmetic totals (the sum of means of each group) are included for illustrative purposes, although it is not suggested that these totals are useful population parameters. It can be seen that the relative contributions of the groups to overall abundance and biomass is not the same in

110 all sites. Ants were the most abundant group in 5 sites (BS3, BS6, BS8, BS10 and BS12), but not at the extremes of the disturbance gradient, where termites (BS1) and earthworms (BS14) predominated. The rank order of macrofaunal abundance was BS1>BS6>BS10>BS3>BS8> BS14>BS12, broadly indicating that the more severe and the more recent disturbances reduced numbers. Earthworms had the highest biomass in 4 sites (in rank order BS10, BS6, BS14 and BS12), whereas termites made the greatest contribution in BS, and macroarthropods other than ants and termites in BS3 and BS8. The rank order of total biomass was BS10>BS6>BS1> BS14>BS8>BS3>BS12. Two disturbed sites, jungle rubber and Paraserianthes plantation, therefore, exceeded the intact rainforest in macrofaunal biomass, in both cases due to the development of large earthworm populations. The overall picture that emerges from these data is of the dominance of termites in intact primary rainforest, and their progressive replacement by other groups, especially ants and earthworms, in disturbed sites. BS12, the alang-alang grassland is clearly the most impoverished site by all measures.

Table 7.8 summarizes the vertical distribution of the soil macrofauna across the 7 Jambi sites. Abundance and biomass are notably concentrated in the top 10 cm of the mineral soil, so that the particular effects of disturbance at this level, whether positive or negative, may be the primary factor determining the responses of soil animals. Sample sizes were considered too small to permit site by site comparisons of the vertical distribution of each group; however, the data are available for such an analysis (not shown). Two ways ANOVA after log transformation indicates a significant interaction between sites and strata for termites and earthworms (Table 7.9).

Table 7.9 shows the results of all ANOVA tests carried out on the soil macrofaunal data. Post hoc pairwise comparisons of abundance and biomass between sites are shown in Tables 7.10 and 7.11, respectively. No widely accepted method of statistical testing exists for soil macrofaunal data, indeed such data are relatively rarely presented even with confidence limits or other indications of variance. Table 7.9 demonstrates a reasonable correspondence between the results of non-parametric and parametric analyses; however, ant abundance and biomass were not significantly different between sites or strata in one-way ANOVAs. Other groups differed overall between sites and strata in both abundance and biomass.

Pairwise comparisons of macrofaunal abundance in sites by Mann-Whitney showed significant differences in 19 out of the 21 possible pairings. Termites were significantly different in 7 comparisons, ants in 7 (of which 1 was unique, i.e. not reflected by other groups), all macrofauna in 10 (of which 2 were unique) and earthworms in 13 (of which 4 were unique). This suggests that earthworm abundance and total macroarthropod abundance were more sensitive in discriminating between sites than ant or termite abundances alone. Pairwise comparisons of macrofaunal biomass showed significant differences in 18 of the possible 21 pairings. Termite biomass was significantly different in 7 comparisons (of which 2 were unique), ant biomass in 4 comparisons (of which 1 was unique), all macrofauna in 8 comparisons (of which 2 were unique) and earthworm biomass in 5 comparisons (of which 5 were unique). Total macroarthropod and earthworm biomass, therefore, were also more sensitive site discriminators than ant or termite biomass alone.

111

Table 7.6 Summary of abundance data for soil macrofauna across a forest disturbance gradient in Jambi Province, Central Sumatra. Numerical densities are given as nos. m-2, based on 5 monoliths per site, spaced along a 40 m transect. For geometric means, data are transformed as log10(x+1), then back-transformed.95% confidence limits (C.Ls.) are given for the geometric means. Arithmetic total is the sum of means.

Abundance parameters BS 1 BS 3 BS 6 BS 8 BS 10 BS 12 BS 14 Primary Logged-over Paraserianthes tree Hevea (rubber) Jungle rubber Degraded Cassava forest forest plantation plantation Imperata garden grassland Ants Arithmetic mean 352 522 550 134 541 80 48 Geometric mean 23 239 223 17 226 15 6 95% C.Ls. 1-1348 28-2004 24-2065 1-529 24-2123 1-833 1-131

Termites Arithmetic mean 2892 163 512 128 211 3 26 Geometric mean 971 65 47 11 25 2 10 95% C.Ls. 190-4966 12-364 1-1923 2-201 2-1107 0-8 0-124

All macroarthropods Arithmetic mean 3668 713 1312 397 830 86 160 Geometric mean 2455 331 630 346 512 30 148 95% C.Ls. 630-9120 86-788 184-2152 177-679 219-1202 2-429 86-253

Earthworms Arithmetic mean 3 6 195 35 576 26 102 Geometric mean 2 2 186 6 565 14 53 95% C.Ls. 1-8 1-14 116-297 1-123 428-743 2-103 12-228

Arithmetic total 3671 619 1507 432 1406 112 262

112 Table 7.7 Summary of biomass data for soil macrofauna across a forest disturbance gradient in Jambi Province, Central Sumatra. Biomass densities are given as g m-2, based on 5 monoliths per site, spaced along a 40 m transect. For geometric means, data are transformed as log10(mg+1), then back-transformed. 95% confidence limits (C.Ls.) are given for the geometric means. Arithmetic total is the sum of means.

Biomass parameters BS 1 BS 3 BS 6 BS 8 BS 10 BS 12 BS 14 Primary Logged-over Paraserianthes tree Hevea (rubber) Jungle rubber Degraded Cassava forest forest plantation plantation Imperata garden grassland Ants Arithmetic mean 0.35 0.29 4.89 0.10 0.86 0.03 0.34 Geometric mean 0.02 0.19 0.35 0.02 0.24 0.01 0.022 95% C.Ls. <0.01-1.43 0.05-0.72 0.01-9.86 <0.01-0.43 0.02-3.09 <0.01-0.16 <0.01-1.22

Termites Arithmetic mean 5.59 0.09 0.59 0.07 0.49 <0.01 0.02 Geometric mean 2.77 0.10 0.47 0.06 0.35 <0.01 0.02 95% C.Ls. 0.09-14.67 0.01-0.26 0.01-1.50 0-0.22 0-1.38 0-0.012 0-0.06

All macroarthropods Arithmetic mean 8.99 1.89 5.79 2.27 6.08 0.64 0.67 Geometric mean 5.08 1.82 2.13 1.55 3.99 0.44 0.66 95% C.Ls. 1.23-20.68 1.09-2.80 0.12-7.79 0.01-5.61 0.67-13.89 0.01-1.76 0.38-1.00

Earthworms Arithmetic mean 0.03 0.06 11.42 0.77 60.16 0.83 4.67 Geometric mean 0.01 0.06 8.40 0.53 33.59 0.12 2.79 95% C.Ls. <0.01-0.05 <0.01-0.09 2.21-26.49 <0.01-2.18 11.92-91.81 0.04-1.22 1.11-11.03

Arithmetic total 9.02 1.95 17.21 3.04 66.24 1.47 5.34

113 Table 7.8 Summary of vertical distribution of invertebrate soil macrofauna in a forest disturbance gradient in Jambi province, Central Sumatra, with statistical analysis of transformed data by parametric analysis of variance (ANOVA). For geometric means data are transformed as log10(x+1) for abundance and log10(mg+1) for biomass, then back-transformed. 95% confidence limits (C.Ls.) are given for the geometric means. ns, not significant.

Population parameter Litter 0-10 10-20 cm 20-30 ANOVA layer cm cm between strata F(3,16) p

ABUNDANCE, nos m-2 Ants Geometric mean 52 136 24 4 2.39 ns 95% C.Ls. 20-134 81-230 8-68 2-75

Termites Geometric mean 15 80 44 4 2.30 ns 95% C.Ls. 3-64 43-148 24-78 1-50

All macroarthropods Geometric mean 184 343 77 38 5.60 <0.01 95% C.Ls. 143-237 153-767 52-119 14-111

Earthworms Geometric mean 0 82 1 0 36.47 <0.001 95% C.Ls. - 47-138 1-7 -

BIOMASS, g m-2 Ants Geometric mean 0.04 0.21 0.03 0.04 1.28 ns 95% C.Ls. 0.01-0.12 0.05-0.92 0.01-0.12 0.01-0.12

Termites Geometric mean 0.06 0.43 0.06 0.03 3.94 <0.05 95% C.Ls. 0-0.14 0.11-1.45 0.03-0.11 0-0.08

All macroarthropods Geometric mean 1.12 1.17 0.23 0.18 8.45 <0.005 95% C.Ls. 0.62-1.97 0.57-2.28 0.12-0.34 0.09-0.32

Earthworms Geometric mean 0 9.67 0.04 0 489.9 <0.001 95% C.Ls. - 7.82-11.88 0-0.10 -

114 Table 7.9 Summary of parametric and non-parametric ANOVA on invertebrate soil marcofaunal abundance and biomass from 4 horizon levels and 7 sites in a forest disturbance gradient. ns, not significant.

Population parameter One-way One-way Two-way parametric ANOVAc Kruskal-Wallis non- parametric parametric parametric ANOVA ANOVA ANOVAd between between sitesa between sites stratab Between Between strata Interaction of sites sites and strata

Ant abundance ns ns p<0.025 p<0.001 ns ns Ant biomass ns ns p<0.05 p<0.025 ns ns Termite abundance p<0.005 ns p<0.001 p<0.001 p<0.05 p<0.025 Termite biomass p<0.025 p<0.05 p<0.001 p<0.001 p<0.01 p<0.025 All macroarthropods p<0.005 p<0.01 p<0.001 p<0.001 ns p<0.005 abundance All macroarthropods p<0.05 p<0.005 p>0.001 p<0.001 ns p<0.025 biomass Earthworm abundance p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 P<0.005 Earthworm biomass p<0.001 p<0.001 p<0.001 p<0.001 p<0.001 P<0.005

aall strata combined for each monolith and data log transformed. beach stratum averaged across 7 sites and data log transformed. cstrata treated as replicate samples in each site and data log transformed. duntransformed data.

115 Table 7.10 Matrix summary of differences between treatments (sites) for invertebrate soil macrofaunal abundance across a forest disturbance gradient in Jambi Province, Central Sumatra. Groups are shown which are significantly different in pairwise comparisons of sites by one-tailed Mann-Whitney. * p<0.05; **p<0.025; ***p<0.005. Numbers in brackets indicate the site with the greater abundance.

BS1

BS3 Termites**(1) Macroarthropods**(3)

BS6 Earthworms***(6) Earthworms***(6)

BS8 Termites**(1) - Earthworms**(6) Macroarthropods**(1)

BS10 Termites**(1) Earthworms***(10) Earthworms***(10) Ants*(10) Macroarthropods**(1) Earthworms***(10) Earthworms***(10)

BS12 Termites***(1) Ants*(3) Ants*(6) Macroarthropods**(8) Ants*(10) Macroarthropods***(1) Termites**(3) Termites*(6) Macroarthropods**(10) Earthworms*(12) Macroarthropods**(6) Earthworms***(10) Earthworms***(6)

BS14 Termites***(1) Ants*(3) Ants*(6) Macroarthropods**(8) Ants*(10) - Macroarthropods***(1) Earthworms**(14) Macroarthropods***(10) Earthworms**(14) Earthworms***(10) BS1 BS3 BS6 BS8 BS10 BS12

116 Table 7.11 Matrix summary of differences between treatments (sites) for invertebrate soil macrofaunal biomass across a forest disturbance gradient in Jambi Province, Central Sumatra. Groups are shown which are significantly different in pairwise comparisons of sites by one-tailed Mann-Whitney. * p<0.05; **p<0.025; ***p<0.005. Numbers in brackets indicate the site with the greater biomass.

BS1

BS3 Termites**(1) Macroarthropods*(1)

BS6 Earthworms***(6) Earthworms***(6)

BS8 Termites***(1) Ants*(3) Earthworms***(6) - - BS10 Termites*(1) Earthworms***(10)

BS12 Termites***(1) Ants**(3) Ants*(6) Macroarthropods*(8) Ants*(10) Macroarthropods**(1) Termites***(3) Termites*(6) Macroarthropods*(10) Macroarthropods*(6) Earthworms***(10) Earthworms***(6) - BS14 Termites***(1) Macroarthropods**(3) Earthworms*(6) Macroarthropods***(10) Earthworms*(14) Macroarthropods***(1) Earthworms**(10) BS1 BS3 BS6 BS8 BS10 BS12

117 Figs. 7.10 and 7.11 represent attempts to examine the functional composition of the soil macrofauna across the seven sites, made on the basis of abundance data means. Unfortunately, we do not have biomass totals for macroarthropods other than ants and termites itemized by taxon, so it is not possible to make the same classifications based on biomass. In Figure 7.10 (Scheme 1) animals are allocated to one of three functional groups reflecting feeding and ingestion habits: engineers, litter transformers and macropredators. Unpigmented worms and termites were designated as engineers; pigmented earthworms, diplopods, crickets, woodlice and cockroaches as litter transformers; and the rest (including all ants) as macropredators. This is a fairly easy classification to make from the available data, but it does involve some over- simplification for certain groups (for example not all ants are predatory). Generic names were available for some earthworms recovered from monoliths, which assisted the allocation of specimens between the engineer and litter transformer category.

Figure 7.10. Proportion (by abundance) of macrofaunal functional groups 1

Other, mostly Engineers Litter transformers macropredators

100 (15)

(<1) (46) (49) (48) (60) (79) (73)

(2) 50 (<1)

(85) (6)

(52) (3) (51) (6) (53)

(34) (21) (19)

0 BS1 BS3 BS6 BS8 BS10 BS12 BS14

Landuse systems

In this scheme there is no clear trend across the gradient, except that disturbed sites are relatively richer in macropredators and impoverished in engineers. In Figure 7.11 (Scheme 2), animals are allocated to one of three functional groups reflecting feeding habits and nesting/burrowing sites: epigeic (non-burrowing, living on the surface), anecic (burrowing but feeding on the surface) and endogeic (burrowing and feeding underground). Allocation is more difficult, as it requires making distinctions between different types of termites and ants, the two groups generally with the greatest abundance. Termites from the monoliths were not identified to species, so it is assumed that those found feeding in the litter are anecic and those feeding in the soil itself are endogeic. The epigeic category is not recognized for termites. For ants, all specimens were identified at least to genus level, and allocation of each to nesting type (above- ground or below-ground) was done anecdotally (by consulting experts) or from a literature search. Above-ground nesters are considered epigeic and below-ground nesters anecic. The endogeic category is not recognized in ants. For this scheme, Figure 7.11 clearly shows an

118 increase in the relative abundance of epigeic forms across the gradient, with the concomitant reduction of endogeics. Overall, disturbance diminishes the proportion of engineers and increases the proportion of epigeic invertebrates. Some amelioration of these trends is evident in the recovering forested sites BS6, BS8 and BS10.

Figure 7.11. Proportion (by abundance) of macrofaunal functional groups 2.

Endogeics Anecics Epigeics

100 (7)

(19)

(47) (55) (55) (67)

(92) (73)

(75) (35) (10) (5) (25)

(10) (35) (28) (19) (20) (4) (17) (4) 0 BS1 BS3 BS6 BS8 BS10 BS12 BS14

Landuse systems

7.7. Synopsis

Figure 7.12 shows means for taxonomic diversity score (defined as in Figure 7.6, with the addition of the number of earthworm species and morphospecies), total abundance and total biomass of soil macrofauna across the seven sites (abundance and biomass presented as geometric means). The trend of diminishing diversity, ameliorated only for BS10, jungle rubber, is the most obvious feature. Surges of abundance and biomass are associated with sites BS6, Paraserianthes plantation and BS10, jungle rubber, reflecting large earthworm populations and/or an abundance of ants. A biomass mean of almost 40 g m-2 (geometric mean) is a feature of the jungle rubber site, and easily exceeds the biomass of any other site. Abundance is greatest in intact rainforest, reflecting the high numerical density of termites, and their reduction in other sites.

7.8 Discussion:

7.8.1 Methods and relevance to Rapid Biodiversity Assessment:

The sampling methods represent the consensus of best practice agreed in 1997 by the Below- Ground Working Group of the Alternatives to Slash-and-Burn Project (ASB). As far as

119 possible, the sampling addresses groups which are known ecosystem engineers (termites, ants and earthworms) and litter transformers (millipedes, woodlice, some coleopterous larvae), rather than surrogate groups or so-called "indicators" with dubious predictive value (see Lawton et al., 1998). In the case of termites, ants and earthworms, the objective is to obtain resolution at species level, and thus contribute elements of true biodiversity to the dataset. The addition of pitfalls is a recent revision of ASB methodology and should assist the achievement of a more representative sampling of ants. Termite species richness is addressed through 100 x 2m qualitative transects (see report by Jones et al., section 8), which take account of the known spatial heterogeneity of termites in forest or forest-derived systems and help to mitigate the variability of data from shorter transects on groups with typically patchy distributions.

Figure 7.12. Biodiversity summary (earthworms included)

50 2500 40

40 2000 30

30 1500

20 1000

10 10 500

0 BS1 BS3 BS6 BS8 BS10 BS12 BS14

Landuse system

Monolith dissection is still the only approach to the sampling of earthworms, and the disturbance involved is known to result in the escape of deep-burrowing species (James, 1996). The various other techniques of earthworm sampling available, for example chemical extraction, vibrational displacement and electrical shocking are considered too cumbersome or environmentally unacceptable for Rapid Biodiversity Assessment, but estimates based on cast frequency and density may have some potential (Hauser et al., 1997; Norgrove et al. 1998; Hauser and Asawalam, 1998). Dry heat extractions, for example, using Berlese, Tullgren or Kempson funnel systems, are suitable for a wide variety of macrofauna (including earthworms) and mesofauna, but are limited by the time required for drying (24-48 hours) and the relative bulkiness and lack of portability of the equipment required, and, thus are not suited for concurrent or sequential rapid assessments over a large geographical range. There is a notable learning-curve effect in the digging of soil monoliths, so that 5 pits can normally be completed

120 comfortably in one day, though sorting extends the time required for each landuse to two days. Similarly, the qualitative termite transect requires 20 man-hours of effort, which usually translates into two days of sampling per land use, including sorting and cleaning of specimens. Neither monoliths nor termite transects can be completed in heavy rain, so a two-day sampling period is really the minimum that can be allocated per land use. The wisdom of starting work early in the day, when light is generally good and downpours less frequent is self-evident. ASB recommends 10 monoliths per transect, but there must be some doubt whether this is a realistic target, since the sorting of the resulting soil samples is a rate-limiting step. It is normally satisfactory to have 2-4 people working on the monolith digging and sorting; a larger number adds to the disturbance of the site and may interfere with other sampling activities taking place concurrently. ASB has now standardized the size of monoliths at 25x25x30cm. 30cm is considered a satisfactory depth, as only a relatively small component of the macrofauna is assumed active below this level in tropical forest zones. However, it should be noted that the assumption will not necessarily be valid in savanna systems.

Macrofauna are only one of five groups of soil organisms which ASB now recommends should be sampled for an adequate assessment of ecosystem function; the others are nematodes, nitrogen-fixing bacteria, mycorrhizal fungi and "decomposers" (i.e. microorganisms having a significant role in the nutrient cycling). Field sampling for these other groups is normally straightforward and rapid, consisting of coring and digging operations of various kinds, but subsequent extraction, cultivation, bioassay and strain-typing procedures impose severe constraints on technical and financial resources (Swift and Bignell, 1999). ASB recognizes that there will be variation in the degree of resolution of taxonomic identity and functional group allocation between the five major target groups of soil biota. This is not considered a problem as long as comparisons are made at the same level of resolution. Standard units have been agreed for expressing data at various levels and this report reflects those agreed for macrofaunas (basically by taxonomic group, functional group, abundance, biomass and biodiversity indices). In the latest (1997) revision of below-ground methods, ASB recommends new functional group classifications for the soil biota as a whole and for termites in particular:

Table 7.12 Functional Group Classifications for the Soil Biota and Termites

Soil biota Termites

ecosystem engineers soil-feeders litter transformers wood-feeders macropredators wood/soil interface feeders micropredators grass-feeders microsymbionts lichen-feeders decomposers fungus-growers

Additional new functional group classifications are to be considered for taxa where the current classifications are considered unsatisfactory, especially earthworms and beetles.

7.8.2 Significance of results at regional and global level:

The literature contains a few reports of tropical ant abundance and biomass (see above). The data obtained from Pasir Mayang are broadly in agreement with expectations. The study of soil

121 fauna on Mount Mulu (Sarawak) by Collins (1980) gave ant abundance in the lowland forest control site at 130 m elevation as 509 m-2 (cf. 352 in the present study) and ant biomass as 0.445 g m-2 (cf. 0.346 in the present study).

Globally, estimates of termite abundance for savannas (>50 to <4000 m2) overlap with those for agricultural systems (>1500 to <6000 m2), secondary forests/plantations (>100 to <10,000 m2), and primary forests (>1000 to <7000 m2). The reliability of individual estimates depends on the rigour of the sampling protocols, but the highest numerical densities are clearly associated with tropical forests. Regional/habitat biomass estimates follow abundance estimates closely, and vary from less than 1 g fresh weight m-2 to a maximum value of 130 g in a near- primary site of the Mbalmayo Forest Reserve (Eggleton et al., 1996). Most site values, however, are less or much less than 10 g m-2, with the highest values in forest systems. Broadly, the numerical and biomass densities of termites are matched with species diversity. Relatively few studies have compared termite abundance and biomass with that of other soil invertebrates in the same systems, but the conclusions of Collins et al. (1984) are probably typical of tropical forests: ants and termites have greater abundance than all other macrofauna put together, and also greater biomass than other macrofauna, excepting earthworms in particularly favourable conditions such as base-rich or neutral soils with high organic content (see also Fogden, 1977; Collins, 1980;1989; Marsh and Wilson, 1981; Lavelle et al., 1997). Given these data, termites may constitute as much as 10% of all animal biomass in the tropics (Wilson, 1993), and as much as 95% of soil insect biomass (Watt et al., 1997). Estimated termite biomass in different biomes is given by Bignell et al. (1997). The relative biomass contribution of termites depends on where they are sampled, as the absolute biomass in African forests is considerably higher than in Asian forests (Table 7.1), declining on a rough longitudinal gradient to <1 g m-2 in rainforests in Australia and Papua New Guinea .

Termite abundance, biomass and species richness are generally reduced when forest is cleared (Collins, 1980; Wood et al., 1982; Eggleton et al., 1995; 1996). However, in some cases temporary nutrient enrichment from cut and abandoned vegetation (Eggleton et al., 1995; 1996; 1997), as well as the availability of termite species from adjacent savannas to colonize disturbed areas (Wood et al., 1982; cf. Eggleton et al., 1996) may obscure overall diversity reductions, as true forest species are replaced with others (i.e. the so-called “trash species”). Less extreme disturbances, such as foraging and hunting by local people, selective logging, conversion to tree plantation, or small-scale subsistence agriculture or agroforestry produce smaller effects on abundance and biomass (and in some cases no effects), but there may be a turnover of species with a tendency for wood-feeding forms to replace soil-feeders. In addition, there may be a lag between the onset of moderate disturbance and noticeable changes in assemblage composition, as colonies can survive for a number of years after disturbance (Eggleton et al., 1996;1997). Feeding group shifts are probably mostly due to changes in canopy cover, with concomitant effects on soil humidity, but both organic C and total N in soils may be reduced along disturbance gradients (Eggleton et al., 1996). Lawton et al., (1998) calculated a modified form of Whittaker’s ß index of species turnover (the ß-2 index, see Harrison et al., 1992) for termites in five sites along a disturbance gradient in the Mbalmayo Forest Reserve ranging from near primary forest to an area completely cleared by bulldozer. This index varies from 0 (no turnover) to 100 (every site has a unique set of species). The value for termites was 28.8, lower than for beetles and canopy ants, but above butterflies, birds, litter ants and soil nematodes.

122 7.8.3 Needs for further work:

Further sampling in sites at both the light and heavy end of the disturbance gradients would be helpful to confirm the apparent benefit to ants and earthworms conferred by intermediate levels of disturbance. Transects with 6, 8 and 10 monolith dissections should also be attempted to see whether slightly or substantially larger sample sizes assist the descriptive and hypothesis- testing statistics, and therefore provide better site-to-site resolution.

7.9 Summary:

7.9.1 Assessments of species richness, abundance and biomass:

These were compared across of range of forest and forest-derived sites representing a disturbance gradient in or adjacent to the Pasir Mayang Forest Reserve, Jambi Province, Sumatra in November 1997. A combination of pitfall traps, dissected soil monoliths (to 30 cm depth) and transects of 100x2 m was used (variously) to assess species richness of ants, termites, other macroarthropods and earthworms; abundance and biomass of these groups were estimated from 5 monoliths (only) arranged along a 40 m transect.

7.9.2 Species richnes,abundance and biomass according to land-use type:

Ants

57 species (Including some morphospecies) from 8 subfamilies were sampled overall, with the highest species richness (33) and taxonomic richness (6 subfamilies) associated with the jungle rubber site (BS10). Highest abundance (arithmetical mean 550 ants m-2) and highest biomass (arithmetical mean 4.889 g m-2) were recorded in the Paraserianthes tree plantation (BS6). Rubber plantation (BS8), alang-alang grassland (BS12) and Cassava garden (BS14) were generally poor sites for ants. Primary forest (BS1) and logged-over forest (BS3) were sites of intermediate species richness, abundance and biomass.

Termites

48 species (including some morphospecies) from 5 subfamilies were sampled overall, with the highest species richness (30) associated with the intact rainforest (BS1), which also had the highest abundance (2892 m-2) and biomass (5.59 g m-2), Rubber plantation (BS8), alang-alang grssland (BS12) and Cassava garden (BS14) were generally poor sites for termites, with jungle rubber (BS10), logged-over forest (BS3) and Paraserianthes tree plantation intermediate for species richness, abundance and biomass. See section 8 of this report.

Other macroarthropods

Arthropods other than ants and termites were of some significance in abundance and biomass totals in several sites, notably intact forest (BS1), logged-over forest (BS3), rubber plantation (BS8) and Cassava garden (BS14).

123 Earthworms

Earthworms had low diversity in Jambi sites, but were numerous and had high biomass in Paraserianthes tree plantation (BS6) and jungle rubber (BS10). In the latter site, numerical density was 576 m-2 and biomass density 60.16 g m-2 (both arithmetic means).

7.9.3 Variability in data collection:

This was high. Transformation of data as log10 (x+1) and the use of non-parametric statistical analysis were considered essential, Many statistically significant differences were found between sites, with earthworms and all macroarthropods being better discriminators at intermediate levels of disturbance. Termites were significantly different between the richest and poorest sites. Abundance and biomass of ants did not differ significantly across the site gradient as a whole; however, it may still be concluded that ant activity and biodiversity are high in sites that are botanically diverse.

7.9.4 Surface concentrationl:

Ants, termites and earthworms were concentrated in the top10 cm of the soil profile, more or less in all sites, to such an extent that sampling of litter and of the soil profile below 10 cm is probably unnecessary. The top 10 cm may, consequently, be a crucial zone for ecosystem processes mediated by the soil fauna, and the effects of disturbance on this layer probably determine the responses of the soil macrofauna as a whole.

7.9.5 Future studies:

These should employ 6-10 monoliths per transect; however the logistical and manpower requirements of such intensive sampling are very demanding.

7.10 References:

Anderson, J.M. and Ingram, J.S.I. (1993) Tropical Soil Biology and Fertility: a Handbook of Methods . CAB International, Wallingford. Barros, M.E., Blanchart, E., Neves A, Desjardins, T., Chauvel, A. & Lavelle, P. (1996) Relaçao entre a Macrofauna e a agregaçao do solo em tres sistemas na Amazonia central. In: Solo/Suelo, XII Congresso Latino Amicano de ciencia do solo. Aguas de Lindoia, S P, Brazil, 4-8 August 1996: Bater, J.E. (1996) Micro- and Macro-Arthropods. pp 163-174 In G.S. Hall (Ed.) Methods for the Examination of Organismal Diversity in Soils and Sediments. CAB International, Wallingford. Baxter, P.F. and Hole, H. (1967) Ant (Formica cinerea ) pedoturbation in a prairie soil. Soil Science Society of America Proceedings 31, 425-428. Belshaw, R., and Bolton, B. (1994) A survey of the leaf-litter ant fauna in Ghana, West Africa (Hymenoptera: Formicidae). Journal of Hymenoptera Research 3, 5-16. Bignell, D.E., Eggleton, P., Nunes, L. and Thomas, K.L. (1997) Termites as mediators of carbon fluxes in tropical forest: budgets for carbon dioxide and methane emissions. In A.D. Watt, N.E. Stork and M.D. Hunter, (Eds) Forests and Insects, pp. 109-133, Chapman and Hall, London. Brussaard, L. & Jumas, N.G. (1996) Organisms and humus insoils. pp 329-359 In Piccolo,A. (Ed.) Humic Substances in Terrestrial Ecosystems. Amsterdam, Elsevier.

124 Collins, N.M. (1980) The distribution of soil fauna on the West Ridge of Gunung (Mount) Mulu, Sarawak. Oecologia (Berl.) 44, 263-275. Collins, N.M. (1989) Termites. In Tropical Rain Forest Ecosystems In H. Lieth and M.J.A. Werger, (Eds.), pp. 455-471, Elsevier Science Publishers, B.V., Amsterdam. Collins, N.M., Anderson, J.M. and Vallack, H.W. (1984) Studies on the soil invertebrates of lowland and montane rain forests in the Gunung Mulu National Park. Sarawak Museum Journal 30, 19-33. Cherrett, J.M. (1989) Leaf-cutting ants: biogeographical, and ecological studies. In. Leith, H., and Werger, M.J. (Eds.) Ecosystems of the World: Tropical Rainforest Ecosystem . pp 473-488,Elsevier, New York. Davidson, D.W. and McKey, D. (1993) The evolutionary ecology of symbiotic ant-plant relationships. Journal of Hymenoptera Research 2, 13-83. Edwards, C.A. (1991) The assesment of populations of soil-inhabiting invertebrates. Agriculture, Ecosystem and Environment 34, 145-176. Eggleton, P., Bignell, D.E., Sands, W.A., Waite, B., Wood, T.G. & Lawton, J.H. (1995) The diversity of termites (Isoptera) under differing levels of forest disturbance in the Mbalmayo Forest Reserve, Southern Cameroon. Journal of Tropical Ecology 11, 85-98. Eggleton, P., Bignell, D.E., Sands, W.A., Mawdsley, N.A., Lawton, J.H., Wood, T.G. & Bignell, N.C. (1996) The diversity, abundance and biomass of termites under differing levels of disturbance in the Mbalmayo Forest Reserve, Southern Cameroon. Philosophical Transactions of the Royal Society of London, Series B. 351, 51-68. Eggleton, P., Homathevi, R., Jeeva, D., Jones, D.T., Davies, R.G. and Maryati, M. (1997) The species richness of termite (Isoptera) in primary and regenerating lowland dipterocarp forest in Sabah, East Malaysia. Ecotropica 3, 119-128. Fogden, M.P.L. (1977) A census of a bird community in tropical rain forest in Sarawak. Sarawak Museum Journal 24, 251-267. Folgarait, P.J. (1996) Latitudinal variation in myrmecophytic Cecropia. Bulletin of the Ecological Society of America 77,143. Fragoso, C., Barois, I., Gonzales, C., Arteaga, C. and Patron, J.C. (1993) Relationship between earthworms and soil organic matter levels in natural and managed ecosystems in the Mexican tropics. pp. 231-240 In Mulongoy K. & Mecchx R. (Eds.) Soil Organic Matter Dynamics and Sustainability of Tropical Agriculture. Chichester, UK. John Wiley and Sons. Garnier-Sillam, E. & Harry, M. (1995) Distribution of humic compounds in mounds of some soil-feeding termite species of tropical rainforests: its influence on soil structure stability.Insectes Sociaux 42, 167-185. Harrison, S., Ross, S.J. and Lawton, J.H. (1992) Beta diversity on geographic gradients in Britain. Journal of Animal Ecology 61, 151-158. Hauser, S. and Asawalam, D.O. (1998). Effect of fallow type and cropping frequency upon quantity and composition of earthworm casts. Zeitschrift für Pflanzenernaehrung und Bodenkunde 161, 267-277. Hauser, S., Vanlauwe, B., Asawalam, D.O. and Norgrove, L. (1997). Role of earthworms in graditional and improved low-input agricultural systems in West Africa. In Brussard, L. and Ferrera-Cerrato (Eds.). Soil ecology in sustainable agricultural systems. CRC Lewis, Boca Raton. Holldobler, B. and Wilson, E.O. (1990). The Ants. Springer, Berlin 732 pp. James, S.W. (1996) Earthworms. pp 249-262 In G.S. Hall (Ed.) Methods for the Examination of Organismal Diversity in Soils and Sediments . CAB International, Wallingford. Jones, D.T. (1999) The response of termites to landuse intensification in lowland Sumatra. In preparation.

125 Lavelle, P. (1988) Earthworm activities and the soil system. Biology and Fertility of Soils 6, 237-251. Lavelle, P. (1996) Diversity of soil fauna and ecosystem function. Biology International 33, 3- 16. Lavelle, P., Blanchard, E., Martin, A., Spain, A.V. & Martin, S. (1992) Impact of soil fauna on the properties of soils in the humid tropics. In Lal, R & Sanchez, P.A. (Eds.) Myths and Science of Soil of the Tropics. pp 157-185 Soil Science Society of America Special Publication 29, Madison, USA. Lavelle, P., Lattaud, C., Trigo, D, Barois, L. (1994) Mutualism and biodiversity in soils. Plant and Soil 170, 23-33. Lavelle, P., Bignell, D.E. and Lepage, M. (1997) Soil function in a changing world: the role of invertebrate ecosystem engineers. European Journal of Soil Biology 33, 159-193. Lawton, J.H., Bignell, D.E., Bolton, B. et al., (1998) Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest. Nature (London) 391, 72-76. Marsh, C.W. and Wilson, W.L. (1981) A Survey of Primates in Peninsular Malaysian Forests. Malaysian Primates research Programme. Universiti Kebangsaan Malaysia. Mulongoy, K. & Bedoret, A. (1989) Properties of worm castes and surface soils under various plant covers in the humid tropics. Soil Biology and Biochemistry 21, 197-203. Murphy, P.W. (Ed) (1962) Progress in Soil Zoology. Butterworths, London. Nash, M.H. & Whitford, W.G. (1995) Subterranean termites: regulators of soil organic matter in the Chihuaharan desert. Biology and Fertility of Soils 19,15-18. Norgrove, L. Hauser, S. & Wiese, S.F. (1998). The effects of cropping density and species upon surface casting by earthworms and implications for nutrient cycling in a tropical intercropping system. Pedobiologia 42, 267-277. Phillipson, J .(Ed.) (1971) Methods for the Study of Productivity and Energy Flow in Soil Ecosystems . IBP Handbook No. 18. Blackwell Scientific, Oxford. Reddy, M.V. & Dutta, M. (1984) Comparative study of some chemical properties of earthworm caste, termite mound and ant gallery materials in relation to the underlying soils of a tropical agroecosystem. Journal of Soil Biology and Ecology 4, 36-40. Scholes, M.C., Swift, M.J., Heal, O.W., Sanchez, P.A., Ingram, J.S.I. & Dalal, R. (1994) Soil fertility research in reponse to the demand for sustainability. In Woomer, P.J. & Swift, M.J. (Eds), The Biological Management of Tropical Soil Fertility, pp 1-14, Chichester, UK. John Wiley and Sons. Smith, F.D.M., May, R.M., Pellew, R., Johnson, T.H. & Walter, K.R. (1993) How much do we know about the current extinction rate? Trends in Ecology and Evolution 8, 375-378. Southwood, T.R.E. (1978) Ecological Methods. Chapman and Hall, London. Stork, N.E. & Brendell, M.J.D. (1993). Arthropod abundance in lowland rain forest of Seram. In: Edwards, I.D., MacDonald, A.A. and Proctor, J. (Eds.) The Natural History of Seram, Maluka, Indonesia. pp.115-130. Intercept, Andover. Swift, M.J. and Bignell, D.E. (1999) Standard Methods for Assessment of Soil Biodiversity and Landuse Practice. Alternatives to Slash-and Burn, Nairobi, Kenya. Swift, M.J. and Mutsaers, H.J.W. (1992) IITA Research for the Humid Forest Zone, 1993-1998 . Ibadan, Nigeria. International Institute of Tropical Agriculture. Van Noordwijk, M., Murdiyarso, D., Hairiah, K. et al., (1997) Forest soils under alternatives to slash-and-burn agriculture. In Schulte, H. (Ed.) Forest Soils . (in press). Watt, A.D., Stork, N.E., Eggleton, P., Srivastava, D. &Bignell, D.E. (1997) Impact of forest loss and regeneration on insect abundance and diversity. In: Watt, A.D., Stork, N.E. and Hunter M.D. (Eds.) Forests and Insects. pp. 271-284, Chapman and Hall, London. Wilson, E.O. (1993) The Diversity of Life. Harvard University Press, Harvard.

126 Wood, T.G. (1996) The agricultural importance of termites in the tropics. Agricultural Zoology Reviews 7, 117-155. Wood, T.G., Johnson, R.A., Bacchus, S., Shittu, M.O. & Anderson, J.M. (1982) Abundance and distribution of termites in a Riparian forest in the southern Guinea savanna vegetation zone of Nigeria. Biotropica 14, 25-39. Woomer, P.L. & Swift, M.J. (Eds.) (1994) The Biological Management of Tropical Soil Fertility. Chichester, UK. John Wiley and Sons.

127

SECTION 8: TERRESTRIAL INSECTS: TERMITES

SPECIES RICHNESS, FUNCTIONAL DIVERSITY AND RELATIVE ABUNDANCE OF TERMITES UNDER DIFFERENT LAND USE REGIMES

D.T. Jones1, F.X. Susilo, D.E. Bignell & H. Suryo

8.1 Introduction:

Indonesia contains more rain forest than any other country in the Asia-Pacific region, and is currently experiencing rapid changes in land use, although exact figures are difficult to obtain. It has been estimated that Indonesia lost between 0.5% to 0.8% of its total closed forest cover per year during the first half of the 1980s (Groombridge, 1992). The equatorial island of Sumatra is the third largest island in the Indonesian archipelago. The island's lowland forests have been heavily logged and large areas are seriously degraded (Riswan & Hartanti, 1995). Sumatra has a relatively high and rising population density and, under growing socioeconomic pressure, large areas of forest have been lost to commercial logging, permanent or shifting subsistence agriculture, or cleared for plantations and transmigration schemes (Whitten et al., 1984). By 1991, Collins et al. estimated about 49% of Sumatra's original forest cover remained, although very little was pristine. In Jambi Province, Central Sumatra, during a six- year period up to 1992, about 8% of primary forest was converted to secondary forest, another 5% was converted to agricultural land, while about 0.3% became grassland (Murdiyarso & Wasrin, 1995).

Much of Sumatra is now a mosaic of different land-use types. Fragments of primary forest remain within large areas of impoverished logged-over and secondary forest, while various silvicultural systems, including vast industrial plantations of oil palm, rubber and fast-growing soft wood tree species, dominate the landscape. Indigenous agroforestry systems vary from cash-crop monocultures to complex multispecies and multi-storey gardens (Aumeeruddy & Sansonnens, 1994). The 'jungle rubber' system is a man-made diverse agroforestry system with a high concentration of rubber trees, which has a forest-like structure when in its mature phase and provides fruits, fuelwood and timber, as well as an income from latex (Gouyon et al., 1993). Intensively farmed and burnt land can be exhausted of nutrients and often reduced to alang-alang (Imperata cylindrica) grassland. Some transmigration farming systems set up on former forested lands have been shown to be unsustainable with the current level of resourcing (Holden et al., 1995).

Within the context of sustainable agricultural production under conditions of rapid land-use change, declining forest cover, loss of biodiversity and an increasing human population, research should be focused on those groups of organisms that contribute directly to plant productivity and their response to changes in land use. The importance of invertebrate macrofauna to the promotion of tropical soil fertility has been stressed in recent reviews

1 Address for correspondence; David T. Jones Biodiversity Division, Entomology Department The Natural History Museum Cromwell Road London, SW7 5BD, UK

128 (Fragoso et al., 1993; Lavelle et al., 1994; Garnier-Sillam & Harry, 1995; Nash & Whitford, 1995; Brussaard & Jumas, 1996; Wood, 1996). The distribution, protection and stabilization of organic matter, the genesis of soil structure, humification, the release of immobilized N and P, the improvement of drainage and aeration, and the increase in exchangeable cations have all been demonstrated in soils modified by termites and earthworms (Lavelle et al., 1997). In African systems, forest clearance depletes termite abundance and diversity (Wood et al. 1982; Eggleton et al.,1995; 1996) but similar studies are not yet available from Asia.

Termites are a key functional group of animals in the tropics and can achieve very high populations. For example, in the forests of southern Cameroon, termites are the most numerous of all insect groups (Watt et al., 1997) with abundances of up to 10,000 m-2, and live biomasses of 100 g m-2 (Eggleton et al., 1996). As the dominant arthropod detritivores, termites are important in decomposition processes (Wood & Sands, 1978; Collins, 1983) and thereby play a central role as mediators of nutrient and carbon fluxes (Jones, 1990; Abbadie et al., 1992; Lawton et al., 1996; Bignell et al., 1997). However, being social insects, termites tend to concentrate around colony centres. These centres are often scattered unevenly through the habitat (for example, see Baroni-Urbani et al., 1978; Gontijo & Domingos, 1991), leading to extreme heterogeneity of individuals and populations.

Given the ecological importance of termites, there is a need to characterize termite assemblage structure within and between sites. As a consequence of their highly patchy spatial distribution, combined with the many and varied field sampling regimes adopted by previous researchers, it has not been possible to use the existing data to make reliable direct comparisons of termite diversity and abundance between sites (see Eggleton & Bignell, 1995). As Sutton & Collins (1991) emphasised, it is necessary to develop and test standardised sampling methods that can be applied easily throughout the tropics. To this end, a standardised transect sampling method designed to measure termite species richness and functional diversity in tropical forests has been developed. The protocol has been used in Cameroon (Eggleton et al, 1995), Thailand (Davies, 1997), Peninsular Malaysia (Jones & Brendell, in press) and two sites in Sabah; Maliau Basin (Jones et al., in press) and Danum Valley (Eggleton et al., in press).

8.2 Aims:

• To assess the termite assemblage under different land uses. The first aim is to measure species richness, functional diversity and the relative abundance of termites under seven different land-use regimes in Jambi Province, central Sumatra. The seven land uses are listed in Section 8.4. By using a standardised sampling protocol, the results from each site can be directly compared, both within this study and with other locations where the transect method has been employed.

• The responses of termites to land-use changes. If the history of exploitation at each of the Jambi study sites is known, it may be possible to arrange the sites along a 'land-use intensification gradient' or into one or more 'land-use sequences'. By assuming that all the Jambi sites were originally forested and had similar termite assemblages, it will be possible to hypothesise about the response of termites to changes in land use. This assumes the primary assumption is correct.

• The search for correlates between termites and other organisms. The multidisciplinary approach adopted in this project is rare in ecological field studies. In all, seven groups of organisms have been studied in the same sites in Jambi Province. These groups are:

129 vascular plants, mammals, birds, termites, butterflies, soil macrofauna (including ants and earthworms), and selected canopy arthropod groups. The third aim is, therefore, to investigate and identify possible correlates between termites and the other target taxa studied in this project.

8.3 Personnel:

Principal Investigator: Dr D.T. Jones (termite ecologist and taxonomist) - Biodiversity Division Entomology Department The Natural History Museum.

Assisted by:

Dr David Bignell (termite physiologist), Tropical Biology & Conservation Unit, University Malaysia Sabah. Dr F.X. Susilo (entomologist) - Jurusan Proteksi Tanaman, Fakultas Pertanian, Universitas Lampung Dr Suryo Hardiwibowo (biologist) - Gadjah Mada Universitas, Yogyakarta, Indonesia

8.4 Methods:

Seven sites in Jambi Province were studied during November 1997, each site representing a distinct land-use type. The seven land-use types and the dates sampled are listed below. One transect was run in each land-use type as follows:

Land use type Site code Date sampled

1. Paraserianthes plantation BS 6 19 + 20 Nov. 1997 2. Primary forest BS 1 21 + 22 Nov. 1997 3. Logged-over forest BS 3 22 + 23 Nov. 1997 4. Imperata grassland BS 12 24 Nov. 1997 5. Cassava garden BS 14 24 + 25 Nov. 1997 6. Jungle rubber BS 10 26 + 27 Nov. 1997 7. Rubber plantation BS 8 27 + 28 Nov. 1997

8.4.1 The standardized transect sampling method:

All transects were co-located with a 40x5m strip transect used to sample vegetation and for other multidisciplinary studies. Each termite transect was 100 m long and 2 m wide, divided into 20 contiguous sections (each 5 m x 2 m), and numbered sequentially. Each section was sampled by two people for 30 minutes (a total of one hour of collecting per section). In order to standardise sampling effort, the trained collectors worked steadily and continuously during the 30 minute collecting period. In each section the collectors searched the following microhabitats which are common sites for termites: surface soil to 5 cm depth; accumulations of litter and humus at the base of trees; the inside of dead logs, tree stumps, branches and twigs;

130 the soil within and beneath very rotten logs; all subterranean nests, mounds, carton sheeting and runways on vegetation, and arboreal nests up to a height of 2 m above-ground level.

The protocol was designed to offer a flexible approach to the sampling, whereby the collectors used their experience and judgement to search for, locate and sample as many species of termite in each section as time allowed. Specimens from each termite population encountered were sampled. All castes were collected if present, but priority was given to finding soldiers and workers. Termites were placed in vials labelled with the section number and filled with 80%ethanol.

In structurally complex habitats, i.e. with a relatively large above-ground biomass (such as forests and plantation systems), the collectors spend approximately half their collecting time searching the above-ground microhabitats described above. The remaining 15 minutes were used searching for termites in the soil. However, in the case of the Imperata grassland (BS 12) and the Cassava garden (BS 14) there was relatively little above-ground biomass. Within the transects in both systems there were no trees and virtually no dead wood or leaf litter. Therefore, in these land-use types (Imperata grassland and the Cassava garden) the collectors sampled only for 15 minutes (total collecting effort = 30 minutes) in each section. This procedure ensured that equal effort was given to searching for termites in the soil in each transect.

The transect sampling method provides a semi-quantitative measure of the relative abundance of termites based on the number of encounters or 'hits' with each species in a transect. A hit is defined as the recorded presence of a species in one section. Therefore, if a species is present in every section of a transect it will have a relative abundance score of 20. The number of hits per transect can then be used as an indicator of the relative abundance of termites occurring within a transect, as well as between transects. It gives no measure of the absolute abundance per unit area.

8.4.2 Identification of material:

During the field trip, great effort was taken to examine as much of the material as time allowed. This was made possible due to the microscope and light source provided by David Bignell. In the evenings, many hours were spent making provisional identifications. All samples with soldiers were identified to genus, and then morphospecies numbers were allocated. A working reference collection was maintained so that material from all transects could be cross- referenced and the morphospecies designations applied consistently. Many vials contained two or more species, and some of these were separated where time and accuracy allowed. Two groups of samples were not identified. The first were samples with workers (i.e. no soldier specimens collected). Workers are difficult and time consuming to identify as the mandibles must be dissected, and the structure of the gut must be examined, sometimes necessitating the removal and mounting of the enteric valve. The second group were genera in the Subulitermes complex. These are small termites whose taxonomy is ill-defined and that are difficult to identify.

It must be stressed that the results given in this report are based solely on the provisional identifications made during the field trip. At the Natural History Museum every sample will be examined again, and accurate species-level identifications will be made. By comparison with the museum's extensive reference collection (which contains approximately 16000 vials of identified material, plus about 1000 vials of type material), it will be possible to put specific

131 names on alarge proportion of the Jambi collection. It is estimated that the identification work at the museum should take about 4 to 5 weeks [note: completed July 1998. eds. See Annex III, Table 11)

Functional groups: Genera were assigned to one of five functional groups based on known feeding habits (see Collins, 1984; Eggleton et al., 1996; Jones et al., in press; Eggleton et al., submitted), the shape of the molar plates of the worker mandibles (Deligne, 1966), and worker gut content analyses (Sleaford et al., 1996). The functional groups are;

• Soil-feeding: termites that feed on humus and mineral soil. • Wood-feeding: termites that feed on dead wood. • Soil/wood interface-feeding: termites that feed on extremely decayed wood that has lost its structure and become soil-like. • Litter-feeding: termites that feed exclusively on leaf-litter and small items of woody trash. • Epiphyte-feeding: Hospitalitermes is known to feed on lichens and other free living non-vascular plants which they graze from the surface of tree trunks (Collins, 1979; Jones & Gathorne-Hardy, 1995).

8.5 Preliminary results:

8.5.1 Species richness:

The preliminary sorting carried out during the Jambi field work produced a conservative total of 23 genera and 48 morphospecies for all seven land-use types (Annex III, Table 11). However, in addition to these taxa, the Subulitermes complex and many vials of workers await examination. The senior author speculates that these vials will possibly contain several genera plus between 3 to 10 species which can be added to the checklist. Members of the Apicotermitinae subfamily are rare inSoutheast Asia but have been collected in transects run in Sabah (Eggleton et al., in press) and Peninsular Malaysia (Jones & Brendell, in press). Within this subfamily the soldiers are absent or rare, however, specimens may be present in the vials of workers from Jambi.

Table 8.1 gives the list of morphospecies currently recorded from each transect. The preliminary identifications clearly show that the primary forest site is the most species rich, while the Imperata grassland and the Cassava garden sites are the most depauperate. The logged-over forest site and the agroforestry systems all have intermediate levels of species richness. Figure 8.1 displays the taxonomic composition of each transect sample. The Termitinae are the dominant subfamily in sites except the Paraserianthes plantation site and the Cassava garden system.

8.5.2 Relative abundance:

The number of hits (the presence of a species in a section) is recorded in Table 8.1. Termites are most abundant in the primary forest site and least abundant in the Cassava garden. The termites collected in this study fall into four feeding groups. Wood-feeding and soil-feeding species are relatively abundant in most transects, while epiphyte-feeders are rare and interface- feeders (those species that feed on extremely decayed soil-like wood) vary considerably in abundance among transects. Figure 8.2 displays the relative abundance of termites in each

132 functional group. Of notable interest is the high relative abundance of soil-feeders in the jungle rubber system, and their absence from the Paraserianthes plantation. Grass-harvesting species and taxa that feed exclusively on leaf-litter appear to be absent from the study sites.

8.6 Discussion:

It must be stressed that the results given in the table and figures are based on provisional identifications. Table 8.1 also lists the number of vials containing specimens of the Subulitermes complex and workers which still await examination, and suggest the possible extent of extra species and hits that may be added to each transect. While we are certain that the final results for most of the transects will vary in species richness and relative abundance from those presented here, the senior author is confident that the overall patterns are likely to be similar to those already evident in the preliminary results.

Our knowledge of the termite fauna of Sumatra is very limited and based on casual sampling (Holmgren, 1913-14; Oshima, 1923, John, 1925; Amir, 1975). Tho (1992) lists a total of 89 species from Sumatra, but this is certainly an underestimate. The development of comprehensive and rigorous sampling techniques produces much higher local species richness estimates than those given by casual collecting methods. For example, after extensive and widespread collecting, Thapa (1981) lists 103 species from Sabah. However, recent research in one area (Danum Valley, South-east Sabah) using transects and labour-intensive sampling regimes produced a checklist of 93 species (Eggleton et al., in press; Homathevi et al., in prep.). Therefore, it is highly likely our studies at Jambi will increase the Sumatran species list.

The transect method has been tested against known local termite faunas and shown to produce representative samples that are not significantly different in taxonomic or functional composition from their local assemblage (Jones & Eggleton, in prep.). The highest species richness found in Southeast Asian forests using the transect method is 33 species at Danum Valley (Eggleton et al., in press). There is a reasonable possibility that the Jambi primary forest transect will exceed the Danum Valley species richness. Changes in the taxonomic and functional composition of the termite assemblages across the seven land-use types will be discussed in detail when the final data set is produced.

The preliminary results show a decline in termites species richness (Fig. 8.1) and relative abundance (Fig. 8.2) across the seven land-use types. Casual observations of the botanical features at each site by the authors suggested a positive relationship between termite species richness and physical complexity. It has been speculated that the degree of canopy closure appears to have a strong influence on termite diversity (Eggleton et al., 1995, 1996). Preliminary results from Jambi show a very high correlation between termite relative abundance and the recorded basal area of woody plants (r2 = 0.95; Gillison, pers. comm.; see also Annex II, Figure 1c). We await the dissemination of the vascular plant data to investigate whether there are significant correlates between the termite assemblages and the plant communities.

The efficiency of the transect method, based on the number of species collected per unit effort (number of days for one trained person to collect and identify samples) has already been calculated (Jones & Eggleton, in prep.). One transect takes one trained collector four days to complete. The material from one primary forest transect at Danum Valley takes one taxonomist about 10 days to sort and identify to species. Given the known levels of species richness and taxonomic difficulty associated with the termite fauna of primary forest in

133 Southeast Asia, we can estimate that 14 days' effort is required for one trained person to run one transect and identify the material. If we make the assumption that the Jambi primary forest transect will have a final richness of 33 species, this equates to an approximate cost of 2.4 identified species per person per day.

8.7 Conclusions:

With the completion of seven termite transects and the preliminary sorting, the field-based phase of the Jambi project can be considered a great success. When all the museum-based identification work is complete, the top set of material will be deposited at the Bogor Museum. A smaller reference collection will be retained by the Natural History Museum. The results of the termite transect study in Jambi will be written-up for publication in an international peer- reviewed journal. This paper will address the first two aims stated in this report, and it will also address partially the third aim (correlates between the termite assemblage and the vascular plant community). This latter line of research is perhaps the most exciting and important theme to be investigated in the Jambi termite project. For the first time it will be possible to relate termite diversity to measured plant parameters. The full set of final results will be sent to Dr Andy Gillison and CIFOR, and it is hoped that at least one joint paper will be produced which investigates correlates between all the groups of organisms studied at Jambi, and the potential usefulness of these groups as target taxa in rapid biodiversity assessment. Acknowledgements

The authors would like to thank Dr Andy Gillison and Ir Nining Liswanti for organising the field work in Jambi and all the travel arrangements. In addition, we are grateful to the logistical support provided by CIFOR and ICRAF while in Indonesia.

8.8 References:

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134 forests. In: S.L. Sutton, T.C. Whitmore, and A.C. Chadwick, eds. Tropical rain forest: Ecology and management , pp. 311-325. Blackwell Scientific Publications, Oxford. Collins, N.M. (1984). The termites (Isoptera) of the Gunung Mulu National Park, with a key to the genera known from Sarawak. Sarawak Museum Journal, 30: 65- 87. Collins, N.M., Sayer, J.A. & Whitmore, T.C., editors (1991). The conservation atlas of tropical forests: Asia and the Pacific, Macmillan Press, London. Davies, R.G. (1997). Termite species richness in fire-prone and fire-protected dry deciduous dipterocarp forest in Doi Suthep-Pui National Park, northern Thailand. Journal of Tropical Ecology, 13: 153-160. Deligne, J. (1966). Caracteres adaptifs au regime alimetaire dans la mandibule des termites (Insectes Isopteres). Compte Rendu d'Academie des Sciences. Paris, 263: 1323-1325. Eggleton, P. & Bignell, D.E. (1995). Monitoring the response of tropical insects to changes in the environment: Troubles with termites. In: R. Harrington and N.E. Stork, eds. Insects in a changing environment, pp. 473-497. Academic Press, London. Eggleton, P., Bignell, D.E., Sands, W.A., Waite, B., Wood, T.G. & Lawton, J.H. (1995). The species richness of termites (Isoptera) under differing levels of forest disturbance in the Mbalmayo Forest Reserve, southern Cameroon. Journal of Tropical Ecology, 11: 85- 98. Eggleton, P., Bignell, D.E., Sands, W.A., Mawdsley, N.A., Lawton, J.H., Wood, T.G. & Bignell, N.C. (1996). The diversity, abundance, and biomass of termites under differing levels of disturbance in the Mbalmayo Forest Reserve, southern Cameroon. Philosophical Transactions of the Royal Society of London, 351: 51-68. Eggleton, P., Homathevi, R., Jeeva, D., Jones, D.T., Davies, R.G. & M. Maryati (in press). The species richness and composition of termites (Isoptera) in primary and regenerating lowland dipterocarp forest in Sabah, east Malaysia. Ecotropica. Fragoso, B.I., Gonzales, C., Arteaga, C. & Patron, J.C. (1993). Relationship between earthworms and soil organic matter levels in natural and managed ecosystems in the Mexico tropics. In: K. Mulongoy and R. Mecchx eds. Soil organic matter dynamics and sustainability of tropical agriculture. pp. 231-240. John Wiley & Sons, Chichester, UK. Garnier-Sillam, E. & Harry, M. (1995). Distribution of humic compounds in mounds of some soil-feeding termite species of tropical rainforests: its influence on soil structure stability. Insectes Sociaux, 42: 167-185. Gontijo, T.A. & Domingos, D.J. (1991). Guild distribution of some termites from Cerrado vegetation in south-east Brazil. Journal of Tropical Ecology, 7: 523-529. Gouyon, A., DeForesta, H. & Levang, P. (1993). Does jungle rubber deserve its name? An analysis of rubber agroforestry systems in south east Sumatra. Agroforestry systems, 22: 181-206. Groombridge, B., editor (1992). Global biodiversity. Status of the Earth's living resources, Chapman & Hall, London, 585 pp. Holden, S., Hvoslef, H. & Simanjuntak, R. (1995). Transmigration settlements in Seberida, Sumatra - deterioration of farming systems in a rainforest environment. Agricultural Systems, 49: 237-258. Holmgren, N. (1913-14). Wissenschaftliche Ergebnisse einer Forschungsreise nach Ostindien, ausgefuhrt im Auftrage der Kgl. Preuss. Akademie der Wissenschaften zu Berlin von H. v. Buttel-Reepen. 3. Termiten aus Sumatra, Java, Malacca und Ceylon. Zoologische Jahrbucher, Abteilungen Systematik, 36: 229-290. John, O. (1925). Termiten von Ceylon, der Malayischen Halbinsal, Sumatra, Java und den Aru- Inseln. Treubia, 6: 360-419. Jones, D.T. & Brendell, M.J.D. (in press). The termite (Insecta: Isoptera) fauna of Pasoh Forest Reserve, Malaysia. Raffles Bulletin of Zoology.

135 Jones, D.T. & Gathorne-Hardy, F. (1995). Foraging activity of the processional termite Hospitalitermes hospitalis (Termitidae: Nasutitermitinae) in the rain forest of Brunei, north-west Borneo. Insectes Sociaux, 42: 359-369. Jones, D. T., J. Tan & Y. Bakhtiar (in press). The termites (Insecta:Isoptera) of the Maliau Basin, Sabah. In: M. Maryati, S. Waidi, A. Anton, M. N. Dalimin and A. H. Ahmad eds., Monograph of the Maliau Basin Scientific Expedition 12-26 May, 1996. Biological Monographs No. 1. Universiti Malaysia Sabah, Kota Kinabalu. Jones, J.A. (1990). Termites, soil fertility and carbon cycling in dry tropical Africa: a hypothesis. Journal of Tropical Ecology, 6: 291-305. Lavelle, P., Lattaud, C., Trigo, D, Barois, L. (1994) Mutualism and biodiversity in soils. Plant and Soil 170, 23-33. Lavelle, P., Bignell, D.E. and Lepage, M. (1997) Soil function in a changing world: the role of invertebrate ecosystem engineers. European Journal of Soil Biology 33, 159-193. Lawton, J.H., Bignell, D.E., Bloemers, G.F., Eggleton, P. & Hodda, M.E. (1996). Carbon flux and diversity of nematodes and termites in Cameroon forest soils. Biodiversity and Conservation, 5: 261-273. Murdiyarso, D. & Wasrin, U.R. (1995). Estimating land-use change and carbon release from tropical forest conversion using remote-sensing techniques. Journal of Biogeography, 22: 715-721. Nash, M.H. & Whitford, W.G. (1995). Subterranean termites: regulators of soil organic matter in the Chihuahuan Desert. Biology and Fertility of Soils, 19: 15-18. Oshima, M. (1923). Fauna simalurensis Termitidae. Capita Zoologica, 2 (3): 1-22. Riswan, S. & Hartanti, L. (1995). Human impact on tropical forest dynamics. Vegetatio, 121: 41-52. Sleaford, F., Bignell, D.E. & Eggleton, P. (1996). A pilot analysis of gut contents in termites from the Mbalmayo Forest Reserve, Cameroon. Ecological Entomology, 21: 279-288. Sutton, S.L. & Collins, N.M. (1991). Insects and tropical forest conservation. In: N.M. Collins and J.A. Thomas eds. pp. 405-425. The conservation of insects and their habitats. Academic Press, London. Thapa, R.S. (1981). Termites of Sabah. Sabah Forest Record, 12: 1-374. Tho, Y.P. (1992). Termites of Peninsular Malaysia. (Kirton, L.G. Ed.). Malayan Forest Records, No. 36: 224 pp. Forest Research Institute Malaysia, Kepong. Watt, A.D., Stork, N.E., Eggleton, P., Srivastava, D., Bolton, B., Larsen, T.B., Brendell, M.J.D. & Bignell, D.E. (1997). Impact of forest loss and regeneration on insect abundance and diversity. In: A.D. Watt and M.D. Hunter eds. pp. 273-286. Forests and Insects. Chapman and Hall, London. Whitten, A.J., Damanik, S.J., Anwar, J. & Hisyam, N. (1984). The Ecology of Sumatra, Gadjah Mada University Press, Yogyakarta, Indonesia. Wood, T.G. & Sands, W.A. (1978). The role of termites in ecosystems. In: M.V. Brian, ed. Production ecology of ants and termites, pp. 245-292. Cambridge University Press, Cambridge. Wood, T.G., Johnson, R.A., Bacchus, S., Shittu, M.O. & Anderson, J.M. (1982). Abundance and distribution of termites (Isoptera) in a riparian forest in the southern Guinea savanna zone of Nigeria. Biotropica, 14: 25-39. Wood, T.G. (1996) The agricultural importance of termites in the tropics. Agricultural Zoology Reviews 7, 117-155.

136 Table 8.1 Species checklist of termites collected from the seven land-use types in Jambi Province, central Sumatra, in November 1997. Termites were collected using the standardised transect sampling protocol. One transect was run in each land-use type. Figures are the relative abundance of each species, based on the number of 'hits' of each species in a transect (the presence of a species in one section represents one hit). Functional group are: W = wood-feeders, I = soil/wood interface-feeders, S = soil-feeders, E = epiphyte-feeders Primary Logged Jungle Rubber Parase- Imperata Cassava Species Functional forest forest rubber pltn. ianthes grassland garden group (BS 1) (BS 3) (BS 10) (BS 8) (BS 6) (BS 12) (BS 14) KALOTERMITIDAE Glyptotermes sp. W - - - 1 - - - RHINOTERMITIDAE Coptotermes curvignathus W 1 1 1 3 1 - - Coptotermes sepangensis W - - - - 4 - - Coptotermes borneensis W - - - - 1 - - Heterotermes tenuior W 1 ------Parrhinotermes near minor W - - 1 - - - - Parrhinotermes near sp. C W - 1 - - - - - Schedorhinotermes javanicus W 1 - 7 - 7 - - Schedorhinotermes sarawakensis W 1 - - - 9 - - Schedorhinotermes tarakanensis W 6 7 4 1 - - - Schedorhinotermes sp. W - - - 2 - - - Primary Logged Jungle Rubber Parase- Imperata Cassava Species Functional forest forest rubber pltn. ianthes grassland garden group (BS 1) (BS 3) (BS 10) (BS 8) (BS 6) (BS 12) (BS 14) TERMITIDAE Macrotermitinae Macrotermes gilvus W ------1 Macrotermes sp. 1 W 1 ------Odontotermes denticulatus W - - 5 - - - - Odontotermes sarawakensis W 10 9 - - - - - Ancistrotermes pakistanicus W - - 3 - - - - Termitinae Prohamitermes mirabilis I 3 7 - 6 4 - - Labritermes buttelreepeni S - - 1 2 - - - Globitermes globosus W 8 4 1 - - 4 - Microcerotermes serrula W 3 7 - 1 - - - Microcerotermes near havilandi W - 1 - - - - - Termes comis I 4 1 - - 1 - - Termes propinquus I 3 - - 12 1 - Homallotermes eleanorae I 1 - - 3 - - - Homallotermes foraminifer I 1 4 - - - - - Mirocapritermes connectens S - 2 10 - - - - Malaysiocapritermes prosetiger S 3 2 10 - - - - Procapritermes neosetiger S - - - 6 - - -

137 Table 8.1 Species checklist of termites collected from the seven land-use types in Jambi Province, central Sumatra, in November 1997.

Primary Logged Jungle Rubber Parase- Imperata Cassava Species Functional forest forest rubber pltn. ianthes grassland garden group (BS 1) (BS 3) (BS 10) (BS 8) (BS 6) (BS 12) (BS 14)

Procapritermes sandakanensis S - - 3 - - - - Procapritermes setiger S 8 6 2 - - - - Procapritermes near minutus S 4 - 1 - - - - Procapritermes sp. A S - - 5 - - - - Coxocapritermes sp. A S 6 1 - - - - - Coxocapritermes sp. C S 2 3 - - - - - Coxocapritermes sp. D S 1 3 2 - - - - Kemneritermes sp. A S 4 1 - - - - - Pericapritermes dolichocephalus S - - 6 - - - - Pericapritermes nitobei S 1 - 2 - - - - Pericapritermes semarangi S 2 - - - - 5 - Dicuspiditermes nemorosus S 11 18 12 12 - - - Dicuspiditermes santschii S 6 5 1 2 2 - - Nasutitermitinae Havilanditermes proatripennis W - - - 6 - - - Nasutitermes havilandi W 1 - 2 - 3 - - Nasutitermes matangensiformis W - - 2 - - - - Nasutitermes neoparvus W - - - 1 - - - Nasutitermes sp. C W - - - 2 - - - Nasutitermes sp. D W 1 - - - 2 - -

Primary Logged Jungle Rubber Parase- Imperata Cassava Species Functional forest forest rubber pltn. ianthes grassland garden group (BS 1) (BS 3) (BS 10) (BS 8) (BS 6) (BS 12) (BS 14)

Bulbitermes germanus W 2 ------Bulbitermes prabhae W 1 ------Bulbitermes sp. A W 3 1 - - - - - Hospitalitermes hospitalis E 4 - - 2 - - - Hospitalitermes sp. G E ------Proaciculitermes ?malayanus S 1 3 - - - - - Proaciculitermes sp. B S 2 3 - - - - - Aciculioiditermes sp. C S 1 ------Oriensubulitermes inanis S 2 4 2 - - - -

Number of species 35 23 22 16 11 2 1 Relative abundance (total hits) 110 94 83 62 35 9 1

138 Figure 8.1. Species richness of termites collected from transects in seven land-use types in Jambi Province, Central Sumatra

30 Nasutermitinae Termitinae

25 Macrotermitinae Rhinotermitidae

s 20

r 15

S 10

0 t r n a n st s o es t d e e e i h a r re b t t r n d o b ta n e la r f fo a ru n ia p g y la r n m ss r ed e e I a a a l p s io r v g g r a t g a m g n e r ta ri o u b a ss P L J b P an a u l C R p

Figure 8.2. Relative abundance of termites collected from transects in seven land-use types in Jambi Province, Central Sumatra

90 Epiphyte-feeders 80 Soil-feeders 70 Interface-feeders

e Wood-feeders

n 60

u 50

i 40

R 30

0 t t r n s a n s s e o e t d e e e b i h a n d r r t t r a r o o b ta n e l a f f ru n a p s g ri s ry d e la n Im a e l p e io ra v a g g s t g a m g n r ra a s ri o u e a t s L J b n a P b P la u p C R

139 SECTION 9: LAND SNAILS

SNAIL FAUNA UNDER VARYING LAND USE TYPES

By J.J. Vermeulen1

As part of a program on lowland forests of the Center for International Forestry Research (CIFOR, Bogor), a number of plots in various land use types in the Jambi lowland region in Central Sumatra were studied. The plots are situated in the Pasir Mayang - Muarabungo area, on low-nutrient ultisoils, at an altitude of 3-80 m asl.

In order to obtain some idea of the snail fauna present, soil samples were collected on 28/29 November 1997 by A. Gillison and E. Pumono (BIOTROP). In each 40x5 m transect, 3 x 1m2 quadrats were sampled (one each at 0, 20 and 40 m points along the transect) and bulked. Loose leaf litter was removed by hand and the soil-litter interface was scraped by hand to a depth of about 3 cm. The samples were stored in plastic bags with holes punched in for aeration. All samples were collected during the start of the wet season, and were moist at the time of collection.

Upon arrival at the laboratory of the author, the samples were first generally assessed. Because all of them appeared either void or extremely poor in snails, it was decided to scan only a standardized portion of each sample (rather than the entire sample) in order to save time. The samples were then sieved to remove the fraction larger than 5 mm. Two samples were floated in water, a technique applied to remove rock pebbles and inorganic silt. Because this hardly reduced the size of the samples, this method was abandoned, and all samples were spread out to dry. After this, the samples were sieved through a 3 mm mesh sieve. The fraction of 3-5 mm was checked entirely for the presence of snails by systematically scanning small amounts spread thinly over a black surface. The fraction smaller than 3 mm was sieved again to remove all dust smaller than 0.8 mm. Then 0.3 liter of this fraction was selected by the method of piling-and-quartering. This 0.3 l sample was checked for snails as described above. A small portion of the fraction smaller than 0.8 mm was also checked.

The results are listed in Table 9.1, below.

Most samples proved indeed void of snails, and even the few containing some shells can be considered extremely poor. Usually, the absence of snails is related to a low pH of the soil, which makes the environment extremely unsuitable for shell-bearing, soil-dwelling snails. The shell of any arboreal snail dying and falling to the forest floor also dissolves rapidly. As a consequence, soil samples without shells do not necessarily indicate the absence of a snail fauna.

However, at least some of the studied soil samples contained one or a few fresh-looking shells of soil-dwelling species (one with the dried animal still inside). This indicates that at least these samples formed a suitable habitat for snails, or can preserve for some period of time the shells of any arboreal species fallen to the forest floor. I, therefore, assume that the samples have been

1 Rijksherbarium/Hortus Botanicus P.O. Box 9514 2300 RA Leiden The Netherlands

140 collected on localities that harbor at most an extremely poor snail fauna, both in species as well as in individuals.

In spite of this, the snail fauna collected are not without significance. The genus Eremopeas, known from northern and central Australia, is well characterized by the sculpture of the top of the shell. It is highly surprising to find a species apparently belonging to this genus on Sumatra. A first assumption would be its unintentional introduction by mankind. However, the Sumatran shells appear different from the two known Australian species. Therefore, the occurrence of a native, yet undescribed species of this genus in Sumatra seems more likely. The material now available (2 juvenile shells) is insufficient for a scientific description of the species. I therefore advise to search plot BS06 for more material; adults are probably slender shells of 5-15 mm high, of whitish or greenish colour. Both empty shells as well as living material preserved in alcohol of this remarkable species are very welcome.

Paropeas achatinaceum is probably native to Sumatra, but occurs widespread throughout SE Asia. Lamellaxis clavulinus are introduced species, originating from Africa. Both species add nothing to the biodiversity value of the plots.

Slugs (snails without a shell) may have been present on the plots. Mainly living on vegetation, they do not leave any trace when dying, and their presence cannot be ascertained by soil samples. Primary forests otherwise void of any snail species are known to harbor species of slugs on Borneo.

The samples consisted mainly of little decayed leaf litter, twigs, fruits, seeds and other organic matter. Rather remarkable was the abundant presence in most samples of small grains of perfectly transparent quartz, often attached to organic matter; some with a rounded but smooth surface, others displaying the typical trigonal crystal faces of this mineral. It is suggested that these quartz grains are possibly autigenous, that they are precipitated in situ in water saturated with SiO2 after percolating through sandy soil.

141

Table 9.1 List of samples and the snail species found per sample.

Code Environment Date of Vol. in Snail species found, and collecting litres* number of specimens

BS10 Jungle Rubber 27/11/97 1.7 void

BS11 Jungle Rubber 27/11/97 1.0 void

BS 01 Primary forest 29/11/97 2.4 void

BS 02 Primary forest 29/11/97 2.4 void

BS 03 Heavily logged rain forest 29/11/97 3.4 void

BS 04 Logged over in 1983 28/11/97 1.3 void

BS 05 Logged over in 1983 28/11/97 1.4 void

BS 06 Paraserianthes plantation 29/11/97 4.4 Eremopeas sp. (2, juv.)

BS 07 Paraserianthes plantation 28/11/97 5.6 Paropeas achatinaceum (1, juv.)

BS 08 Rubber plantation 28/11/97 1.2 Lamellaxis gracilis (1, fragm.)

BS 09 Rubber plantation 28/11/97 1.7 void

BS 16 Chromolaena fallow 27/11/97 1.5 Lamellaxis gracilis (1, adult) Paropeas achatinaceum (1, juv.)

* Volume in litres, after removing the fraction >5 mm.

142

SECTION 10: SOIL PROPERTIES AND CARBON STOCKS

Kurniatun Hairiah1 and Meine van Noordwijk2 1Brawijaya University, Malang (Indonesia), 2ICRAF S.E.Asia, Bogor (Indonesia)

10.1. Introduction:

The integrated biodiversity survey compared a range of land-use practices in the Bungo-Tebo district in the lowland peneplain of Jambi. The landscape consists of an undulating plain, formed as marine sediment in the tertiary period (Van Noordwijk et al., 1995, 1997b). Most of the land in the interfluves is covered by highly leached oxisols/ultisols, with more recent sediment and generally higher fertility near the rivers where inceptisols and entisols dominate. The survey was intended to highlight the effects of land use on biodiversity, so variation in soil types would be minimized in the selection of sample points. As older human settlements, and hence an important land use type in the form of extensive rubber agroforest, are usually found close to the streams and rivers, not all sampling points could be located in the oxisol/ultisol complex.

Data on conservative soil properties such as texture, pH and exchangeable cations were collected to check the extent to which all variation in biodiversity can be attributed to land use and management, rather than to a priori differences in soil and vegetation. Soil organic matter content and bulk density are likely to be influenced by land use, and may themselves become factors influencing development of vegetation and ecosystem function. The above-ground biodiversity sampling protocol (Gillison et al., this volume) includes an estimate of woody plant basal area. For the full characterization of terrestrial carbon stocks the ASB project has developed a protocol quantifying biomass in trees, understorey vegetation, surface litter and dead wood, and soil carbon in the top 30 cm of the profile. Data were collected with this protocol to help calibrate the simpler assessment of woody plant basal area.

Decline of soil organic carbon content of (former) forest soils after forest conversion is a major concern, both for the on-site fertility of such soils and for estimating the impacts of land-use change on the global C balance in the context of climate change. Effects of land-use change on soil organic carbon (Corg), may be difficult to quantify from limited datasets, as generally no historical data are available of Corg before forest conversion, and one normally has to rely on 'paired' datasets of sites still under forest and those now under other land uses. Even moderate differences in soil texture and/or pH, however, can lead to changes in Corg of similar magnitude as those of the land use change. Van Noordwijk et al. (1999) proposed to use a ratio of the measured Corg and a reference Corg value for forest (top) soils of the same texture and pH as a 'sustainability indicator'. A substantial dataset of soils on Sumatra (Indonesia) was used to derive a pedotransfer function for such a reference value (Van Noordwijk et al., 1997a).

143 10.2. Methods:

Methods for quantifying carbon stocks were used as specified in the ASB protocol (Palm et al., 1994). For the vegetation and soil macrofauna, the sampling area was based on the 40 x 5 m2 transect, as before. All tree diameters above 5 cm in the forest plots were measured by the BIOTROP team and data were converted into aboveground biomass with an allometric equation modified from Brown (1997) on the basis of additional data collected in the Jambi area (Ketterings et al., in prep.):

Y (kg tree-1) = 0.092 Diam 2.60 where tree diameter (Diam) is measured in cm. Understorey and herbaceous layer vegetation was measured in eight 0.25 m2 quadrat samples (or four 1-m2 samples for non-forest plots); total fresh weight was measured, and subsamples were collected for determining dry matter content. Diameter and length of dead wood (> 5 cm diameter) were measured within the 40 x 5 m2 transect and converted to volume on the basis of a cylindrical form; three apparent density classes were used and ring samples were taken to assess the dry weight bulk density (g cm-3) of the partly decayed wood . Surface litter (including wood < 5 cm diameter) was collected down to the surface of the mineral soil in eight 0.25 m2 samples. To remove mineral soil particles, the litter samples were washed and sundried; subsamples were taken for dry matter content.

Soil bulk density was measured for the 0-5 cm top soil layer (8 replicates per sampling point) by carefully inserting a 165 cm3 ring from the mineral soil surface, just below the litter layer.

Soil samples were collected (composited from 8 sample points per 200 m2 sampling area) for the 0-5, 5-10, 10-20, 20-30 cm depth zone below the litter layer, passed through a 2 mm sieve and air-dried for analysis of texture (sand, silt, clay), pH (1N KCl), pH(H2O), P BrayII, Corg (Walkey and Black), Ntot (Kjeldahl), exchangeable K, Ca, Mg, Na, Al and H, and effective cation exchange capacity (ECEC) by summation. All these routine soil measurements were done on air-dried, sieved soil in the soils laboratory of Brawijaya University (Malang, Indonesia) with methods consistent with those described in Anderson and Ingram (1993). In addition, a size- density fractionation of macro-organic matter based on Ludox solutions of various densities was used, as described by Hairiah et al. (1995, 1996a) and Meijboom et al. (1995), for the 0-5 and 5-10 cm depth zone. The reference value for Corg (‘Cref’) was calculated on the basis of soil texture on the basis of a large data set of Sumatran soils (Van Noordwijk et al. 1997a, 1998, 1999).

10.3. Field notes on sampling points:

Primary forest (BS 1,2) - two samples behind the permanent forest plots of BIOTROP but in the 25 ha reserve; the plots are on two sides of a small stream.

144 Logged-over forest (BS 3,4,5) - three samples: no. 3 close to the second primary forest plot, on a ridge with logging track overgrown by ferns, secondary forest regrowth and patches of undisturbed forest; no. 4 and 5 in the logged-over forest (1983) where BIOTROP has permanent plots; no. 4 includes a recent tree fall, no. 5 appears to be little affected by the logging. Industrial timber plantation (HTI) (BS 6,7) - 5-year old Paraserianthes falcataria plantation; no. 6 close to the road and forest edge, no. 7 in the centre of the HTI area; (the Paraserianthes still seemed to be affected by a moth). Rubber plantation (BS 8,9) - 8-year old intensively managed rubber established by slash-and-burn from logged-over forest, along the main logging road in Pasir Mayang; both plots are part of a 18 ha farm established by a former employee of PT IFA, and currently partly operated by share-tappers; the plantation was established from seedlings obtained from the plantation project across the river (GT1 ?) and was managed in plantation-style (but without legume cover crops). Jungle rubber (BS 10,11) - a 45 (?) year old rubber agroforest in Dusun Tuo (across the Batang Hari river from Pasir Mayang), in a landscape with a lot of newly planted rubber (mostly seedlings). Imperata grassland (BS 12,13) - in Kuamang Kuning, close to the Imperata plots sampled in 1996. Cassava (BS 14,15) - in Kuamang Kuning, close to Imperata plots; part of the fields was opened by tractor, apparently for planting oil palm. Chromolaena fallow (BS 16) - in Dusun Tuo, close to the jungle rubber (10 and 11); a 3 (?) year old fallow, about to be re-opened for planting rice.

10.4. Results and Discussion:

Soil characteristics are summarized in Table 10.1. Soil texture data show that the sampling points belong to essentially three groups:

• soils with less than 20% clay in the top 5 cm (sampling points BS 1, 2 ,4, 5 and 6), • soils with 20-40% clay in the top 5 cm (BS 3, 7, 10, 11, 12, 13, 14, 15 & 16), • soils with more than 40% clay in the top 5 cm (sampling points BS 8 and 9).

These differences are probably a priori and not caused by current land use. The location of the rubber plantation (8&9) on a soil of higher clay content is probably typical for the position of rubber in the landscape. Comparisons between sites in different classes have to take these soil differences into account.

All sites were acid, with the highest pH (H2O) values found in the Imperata and Cassava sites around the transmigration village, possibly indicative of past lime applications (note that pH(KCl) values show less variation) and the Chromolaena fallow plot.

Soil organic carbon (Corg) and total N (Ntot) showed a strong decrease with depth, justifying the separation of the 0-5 and 5-10 cm depth layer. Available soil phosphorus levels were very low in sample 8, and relatively high in 10 and 11. The -1 effective cation exchange capacity was low (< 12 cmole kg ) in all soils. Al saturation

145 was high in all soils, but lowest in sites 12 and 13. Overall, a weak buyt statistically significant relationship was found between Al-saturation and pH(H20):

2 Al-sat = 104.0 – 12.5 * pH(H2O) [n = 63, r = 0.23, P < 0.001]

Al-sat = 99.2 – 14.8 * pH(KCl [n = 63, r2 = 0.05, P = 0.045]

Bulk density measurements (Table 10.2) showed substantial differences between the plots; tracks in the logged over forest, the young industrial timber plantation and the Cassava and Imperata plots had a bulk density substantially higher than that of natural forest; the logged over forests outside the skidding track had a high coefficient of variation in bulk density, indicating patch-wise soil compaction

The differences between Corg of the topsoil between the sampling points probably reflect differences in soil texture as well as land use. When the Corg/Cref ratio is compared, the data appear to reflect land use effects more clearly (compare Figure 10.1A and 10.1C). The size/density fractionation data (Figure 10.1C) failed to differentiate clearly between the land uses.

146

Table 10.1. Measured soil parameters

No. LUT Depth Texture pH_ pH_ C_org N_tot C/N P_brayII Exchangeable cations ECEC Al_sat Sand Silt Clay H2O KCl ratio K Na Ca Mg Al H -1 -1 cm % % % mg kg cmole kg % 1 NF 0_5 62 24 14 4.0 3.5 4.01 0.28 14.3 10.2 0.16 0.34 1.65 0.41 4.19 1.16 7.91 53.0 1 NF 5_10 62 20 18 4.7 3.8 1.86 0.14 13.3 4.19 0.09 0.24 1.54 0.51 4.19 0.85 7.42 56.5 1 NF 10_20 62 20 18 4.9 3.9 1.20 0.09 13.3 2.09 0.08 0.22 1.54 0.10 3.59 0.89 6.42 55.9 1 NF 20_30 64 18 18 4.9 4.0 0.80 0.06 13.3 1.69 0.06 0.22 1.03 0.07 3.53 0.83 5.74 61.5 2 NF 0_5 67 22 11 4.2 3.5 3.21 0.19 16.9 9.19 0.19 0.31 1.54 0.62 3.71 1.27 7.64 48.6 2 NF 5_10 69 19 12 4.7 3.8 2.01 0.13 15.5 6.69 0.11 0.24 1.54 0.10 3.53 0.83 6.35 55.6 2 NF 10_20 66 17 17 4.8 3.7 1.61 0.12 13.4 2.69 0.11 0.23 3.61 1.03 3.17 0.93 9.08 34.9 2 NF 20_30 67 17 16 4.8 4.0 0.96 0.07 13.7 1.69 0.09 0.20 1.54 0.1 2.99 1.06 5.98 50.0 3 LOF 0_5 54 8 38 4.5 3.7 1.85 0.13 14.2 2.69 0.12 0.25 1.55 0.51 2.93 0.8 6.16 47.6 3 LOF 5_10 81 10 9 5.2 3.8 1.53 0.12 12.8 5.19 0.10 0.29 2.06 0.21 2.69 0.24 5.59 48.1 3 LOF 10_20 67 13 20 5.0 4.0 1.36 0.11 12.4 4.69 0.08 0.20 1.03 0.51 2.69 0.74 5.25 51.2 3 LOF 20_30 65 13 22 4.8 4.0 1.20 0.08 15.0 3.16 0.06 0.18 1.02 0.51 3.02 0.99 5.78 52.2 4 LOF 0_5 81 11 8 4.5 3.6 4.66 0.28 16.6 18.0 0.15 0.25 1.12 1.02 4.15 1.09 7.78 53.3 4 LOF 5_10 79 10 11 4.0 3.5 3.13 0.18 17.4 5.19 0.11 0.25 1.55 1.34 3.29 1.38 7.92 41.5 4 LOF 10_20 77 10 13 4.6 3.7 2.09 0.12 17.4 3.69 0.09 0.25 2.57 0.41 3.29 1.38 7.99 41.2 4 LOF 20_30 74 10 16 4.7 3.7 1.85 0.12 15.4 2.69 0.08 0.28 2.37 0.21 3.41 0.95 7.30 46.7 5 LOF 0_5 79 13 8 4.2 3.3 4.41 0.28 15.8 6.19 0.20 0.39 2.06 0.31 2.69 1.65 7.30 36.8 5 LOF 5_10 79 13 8 4.5 3.8 1.91 0.12 15.9 6.13 0.10 0.28 1.12 1.22 2.97 0.97 6.66 44.6 5 LOF 10_20 76 11 13 4.8 3.9 1.61 0.10 16.1 4.65 0.07 0.22 1.33 0.41 2.97 0.73 5.73 51.8 5 LOF 20_30 75 15 10 4.8 4.0 1.27 0.10 12.7 4.15 0.07 0.16 1.22 0.61 2.67 0.66 5.39 49.5 6 HTI 0_5 84 8 8 4.4 3.9 2.78 0.17 16.4 18.5 0.18 0.38 2.04 0.61 2.61 0.47 6.29 41.5 6 HTI 5_10 82 10 8 4.3 3.9 2.15 0.13 16.5 9.10 0.06 0.19 1.33 1.22 2.67 0.72 6.19 43.1 6 HTI 10_20 79 8 13 4.8 4.0 1.67 0.10 16.7 5.64 0.06 0.14 1.54 1.02 2.31 0.77 5.84 39.6 6 HTI 20_30 74 10 16 4.8 4.1 0.50 0.05 10.0 2.66 0.04 0.13 1.22 0.31 2.55 0.60 4.85 52.6

147 Table 10.1. Measured soil parameters

No. LUT Depth Texture pH_ pH_ C_org N_tot C/N P_brayII Exchangeable cations Al sat Sand Silt Clay H2O KCl ratio K Na Ca Mg Al H ECEC -1 -1 cm % % % mg kg cmole kg % 7 HTI 0_5 46 28 26 5.2 3.8 4.21 0.28 15.0 8.78 0.41 0.62 4.68 1.56 1.33 0.87 9.47 14.0 7 HTI 5_10 45 19 36 5.2 3.9 2.11 0.16 13.2 1.20 0.21 0.45 4.16 1.14 1.89 0.21 8.06 23.4 7 HTI 10_20 43 22 35 4.8 3.6 1.78 0.14 12.7 0.69 0.19 0.43 3.12 1.04 4.23 0.80 9.81 43.1 7 HTI 20_30 43 22 35 4.8 3.6 1.62 0.11 14.7 0.19 0.12 0.38 1.87 1.25 5.14 0.90 9.66 53.2 8 RUB_P 0_5 14 27 59 4.6 3.5 5.97 0.38 15.7 1.20 0.19 0.36 2.41 0.95 3.96 2.07 9.94 39.8 8 RUB_P 5_10 14 11 75 4.5 3.7 2.95 0.18 16.4 0.19 0.12 0.29 2.10 0.31 2.81 1.25 6.88 40.8 8 RUB_P 10_20 12 16 72 4.9 3.7 1.96 0.13 15.1 0.19 0.12 0.33 1.68 0.41 2.81 0.86 6.21 45.2 8 RUB_P 20_30 11 13 76 4.9 3.8 1.86 0.12 15.5 0.19 0.1 0.32 1.52 0.94 1.63 0.71 5.22 31.2 9 RUB_P 0_5 15 41 44 4.4 3.6 3.27 0.53 6.2 10.0 0.27 0.38 1.78 0.59 5.67 1.89 9.40 60.3 9 RUB_P 5_10 13 15 72 4.8 3.7 2.41 0.31 7.8 7.50 0.13 0.36 1.62 0.42 3.23 1.21 7.65 42.2 9 RUB_P 10_20 13 18 69 4.7 3.9 2.19 0.16 13.7 1.25 0.09 0.18 1.80 1.08 3.14 1.04 7.50 41.9 9 RUB_P 20_30 12 23 65 4.5 3.9 2.13 0.14 15.2 0.18 0.05 0.17 1.57 0.63 3.36 1.08 6.82 49.3 10 J_RUB 0_5 6 70 24 5.2 3.8 6.23 0.46 13.5 41.5 0.51 0.69 2.37 0.76 5.31 2.63 10.7 49.5 10 J_RUB 5_10 7 58 35 5.1 3.8 3.97 0.28 14.2 17.2 0.23 0.63 2.12 0.42 5.05 1.49 11.1 45.6 10 J_RUB 10_20 5 54 41 5.1 3.8 2.81 0.22 12.8 10.5 0.22 0.37 1.59 0.21 4.93 1.48 8.81 56.0 10 J_RUB 20_30 5 46 49 5.1 3.8 2.13 0.19 11.2 4.78 0.13 0.31 1.26 0.31 4.88 1.15 8.37 58.3 11 J_RUB 0_5 9 52 39 5.4 3.9 5.76 0.37 15.6 32.8 0.46 0.68 2.46 0.33 3.39 1.76 8.47 40.0 11 J_RUB 5_10 9 50 41 5.3 3.9 3.20 0.27 11.9 10.2 0.25 0.45 1.71 0.23 3.98 1.53 8.38 47.5 11 J_RUB 10_20 9 42 49 5.2 3.8 2.44 0.23 10.6 5.44 0.25 0.42 1.84 0.32 3.77 1.26 8.13 46.4 11 J_RUB 20_30 7 33 60 5.1 3.8 2.11 0.20 10.6 1.30 0.27 0.52 1.72 0.34 3.10 1.02 7.21 43.0 12 IMP 0_5 66 14 20 5.8 4.1 2.19 0.13 16.8 8.27 0.20 0.36 1.56 1.04 1.21 0.05 5.39 22.4 12 IMP 5_10 67 11 22 5.5 4.2 2.03 0.12 16.9 6.25 0.12 0.37 1.35 0.41 1.03 0.61 3.33 30.9 12 IMP 10_20 69 9 22 5.3 3.8 1.78 0.10 17.8 1.20 0.11 0.31 1.35 0.73 1.51 0.31 4.62 32.7 12 IMP 20_30 61 13 26 5.2 3.9 1.22 0.09 13.6 1.20 0.05 0.22 1.56 0.52 2.00 0.39 4.66 42.9 13 IMP 0_5 66 13 21 5.7 4.0 2.23 0.13 17.2 4.15 0.09 0.42 1.12 0.51 1.18 0.67 3.71 31.8 13 IMP 5_10 67 5 28 5.6 4.0 2.10 0.12 17.5 3.16 0.20 0.45 1.12 0.71 1.48 0.68 4.63 32.0

148 Table 10.1. Measured soil parameters

No. LUT Depth Texture pH_ pH_ C_org N_tot C/N P_brayII Exchangeable cations Al sat Sand Silt Clay H2O KCl ratio K Na Ca Mg Al H ECEC -1 -1 cm % % % mg kg cmole kg % 13 IMP 10_20 65 8 27 5.4 4.0 2.07 0.12 17.3 2.66 0.18 0.44 1.72 0.41 1.78 0.19 5.21 34.2 13 IMP 20_30 65 8 27 5.4 4.0 1.51 0.09 16.8 1.67 0.14 0.41 1.34 1.02 1.78 0.38 4.88 36.5 14 CAS 0_5 61 16 23 5.0 3.8 1.51 0.11 13.7 18.0 0.11 0.25 1.02 0.81 2.19 0.09 4.76 46.0 14 CAS 5_10 57 16 27 5.0 3.8 1.27 0.10 12.7 6.13 0.10 0.24 1.63 0.94 2.07 0.82 5.07 40.8 14 CAS 10_20 54 19 27 5.0 3.8 0.97 0.09 10.8 2.21 0.06 0.23 2.29 0.63 2.12 0.71 6.15 34.5 14 CAS 20_30 51 16 33 4.8 3.8 0.49 0.05 9.8 0.19 0.05 0.22 1.56 1.04 2.48 0.41 6.06 40.9 15 CAS 0_5 68 13 19 5.1 3.9 1.78 0.12 14.8 17.4 0.11 0.36 2.08 0.45 1.51 0.50 4.92 30.7 15 CAS 5_10 61 18 21 5.1 3.8 1.70 0.11 15.5 7.77 0.11 0.34 1.56 0.52 1.51 0.69 4.54 33.3 15 CAS 10_20 60 16 24 5.2 3.9 1.62 0.10 16.2 6.76 0.11 0.31 1.56 1.04 1.81 0.70 5.52 32.8 15 CAS 20_30 60 16 24 5.2 3.9 1.38 0.10 13.8 4.23 0.08 0.29 1.56 0.41 1.81 0.70 4.85 37.3 16 CHRO 0_5 9 66 25 5.7 4.2 4.66 0.32 14.6 35.1 0.48 0.88 2.64 2.41 1.20 0.88 8.31 14.4 16 CHRO 5_10 9 59 32 5.3 3.9 3.64 0.28 13.0 17.9 0.28 0.71 2.28 0.57 2.65 1.49 7.37 36.0 16 CHRO 10_20 6 57 37 4.9 3.8 2.72 0.20 13.6 6.49 0.27 0.61 2.96 0.22 2.85 1.43 8.40 33.9 16 CHRO 20_30 10 52 38 4.8 3.7 2.27 0.16 14.2 3.37 0.12 0.54 1.62 0.54 3.45 1.12 7.70 44.8

149

Table 10.2. Bulk density (g cm-3) of the top 5 cm based on 8 replicates per sampling point

Number Code Mean Standard Coefficient Standard error deviation of variation of mean BS01 NF 0.67 0.164 0.245 0.06 BS02 NF 0.69 0.141 0.203 0.05 BS03 LOF 0.87 0.377 0.434 0.13 BS04 LOF 0.75 0.155 0.206 0.05 BS05 L0F 0.69 0.268 0.386 0.09 BS05 TRACK 1.20 0.218 0.181 0.08 BS06 HTI 1.01 0.155 0.154 0.05 BS07 HTI 1.00 0.108 0.107 0.04 BS08 RUB_P 0.79 0.069 0.088 0.02 BS09 RUB_P 0.66 0.138 0.208 0.05 BS10 J_RUB 0.65 0.063 0.097 0.02 BS11 J_RUB 0.73 0.103 0.141 0.04 BS12 IMP 1.12 0.076 0.068 0.03 BS13 IMP 1.26 0.089 0.071 0.03 BS14 CAS 1.31 0.142 0.108 0.05 BS15 CAS 1.16 0.146 0.126 0.05 BS16 CHROM 0.77 0.079 0.103 0.03

Table 10.3. Soil organic matter data compared to the reference value Cref (based on regression of Corg on soil texture fore a large data set of Sumatran soils) and results of the size/ density fractionation of soil with Ludox

Depth Corg Cref Corg/Cref Light Intermediate Heavy LUT % % g kg-1 g kg-1 g kg-1 0-5 cm Nat.Forest 3.88 2.84 1.37 6.68 8.11 1.81 Logged F. 3.26 2.91 1.20 2.06 5.64 1.73 R.Agroforest 6.00 4.36 1.38 1.87 3.31 0.90 Rub.plant. 4.62 4.62 0.99 3.25 5.90 8.59 Timb.plant. 3.50 2.83 1.23 5.94 7.15 1.63 Cassava 1.65 2.84 0.58 1.22 1.41 2.13 Imperata 2.21 2.72 0.81 0.72 1.35 2.11 Chromolaena 4.66 4.01 1.16 1.08 5.53 0.92 5-10 cm Nat.Forest 1.93 2.69 0.72 0.96 1.21 0.52 Logged F. 2.33 2.54 0.91 1.23 3.17 1.24 R.Agroforest 3.59 4.43 0.81 0.59 0.44 0.63 Rub.plant. 2.68 4.84 0.55 0.43 0.60 0.28 Timb.plant. 2.13 2.88 0.76 1.31 4.50 1.58 Cassava 1.49 3.00 0.50 0.68 1.37 1.86 Imperata 2.07 2.71 0.76 0.29 2.28 1.41 Chromolaena 3.64 4.28 0.85 0.48 0.87 2.15

150 Corg 7 6 0-5 cm 5-10 cm 5

3 2

1 0

Impe r ata Impe r ata Cassava Cassava Logged F. Rub.plant. Logged F. Rub.plant. Nat.Forest Nat.Forest Timb.plant. Timb.plant. R.A grof orest Chromolaena R.A grof orest Chromolaena

Corg/Cref 1.6 1.4 0-5 cm 5-10 cm 1.2

0.8 0.6 0.4 0.2 0

Imp e r a ta Imp e r a ta Cassava Cassava Logged F. Rub.plant. Logged F. Rub.plant. Nat.Forest Nat.Forest Timb.plant. Timb.plant. Chromolaena R.A grof orest Chromolaena R.A grof orest

Fraction, g/kg 20 18 0-5 cm 5-15 cm heavy 16 14 Inter m 12 10 Light 8 6 4 2 0

Impe r ata Impe r ata Cassava Cassava Logged F. Rub.plant. Logged F. Rub.plant. Nat.Forest Nat.Forest Timb.plant. Timb.plant. R.A grof orest Chromolaena R.A grof orest Chromolaena

Figure 10.1. Indicators of soil organic matter saturation: A.l; Corg, B. Corg/Cref, C. Size- density fractions (LUDOX method), grouped by land use.

151

Table 10.4 Dry weights and C stocks for the 16 sampling points

Dry weight kg m-2 C-stock kg m-2 BS Code SystAge, Dead Litter Green Trees Necromass Biomass Soil Total No. year wood biomass 0-20 cm 1 NF 100.0 21.31 1.37 0.13 93.60 10.21 42.18 3.33 55.72 2 NF 100.0 4.16 1.28 0.00 88.50 2.45 39.83 3.58 45.85 3 LOF 15.0 16.76 1.50 0.00 11.30 8.21 5.09 3.13 16.43 4 LOF 15.0 22.19 0.91 0.05 25.80 10.40 11.63 5.46 27.49 5 LOF 100.0 1.26 1.11 0.01 86.20 1.07 38.79 3.95 43.81 6 HTI 5.0 14.94 1.78 0.25 8.00 7.53 3.71 4.53 15.77 7 HTI 5.0 0.56 1.03 0.09 9.45 0.71 4.29 5.18 10.18 8 RUB_P 10.0 7.67 0.77 0.11 13.70 3.80 6.21 5.83 15.84 9 RUB_P 10.0 12.30 0.68 0.08 17.80 5.84 8.05 4.30 18.19 10 J_RUB 35.0 13.50 0.62 0.03 21.60 6.35 9.73 6.51 22.60 11 J_RUB 35.0 2.02 0.91 0.02 28.70 1.32 12.92 6.23 20.48 12 Imp 1.0 0.00 0.11 0.23 0.00 0.05 0.10 4.53 4.68 13 Imp 1.0 0.00 0.09 0.18 0.00 0.04 0.08 5.46 5.58 14 Cas 0.5 0.00 0.06 0.21 0.00 0.03 0.09 3.16 3.28 15 Cas 0.5 0.00 0.04 0.29 0.00 0.02 0.13 4.08 4.22 16 Chrom 3.0 0.00 0.56 0.34 0.00 0.25 0.15 6.42 6.82

Table 10.4 summarizes data on the above and belowground carbon stocks for all sampling points. The total values for the forest plots (around 50 kg m-2, corresponding to 500 Mg ha-1) are consistent with other data for lowland forests sampled in the ASB project (Woomer et al., 1998?). The logged over forests had substantially lower biomass AC stocks, but partly made up fror the difference by high dead wood (necromass) stocks.

152

45 40 Biomass 35 Necromass 30 25 20 15 10

Total C stock, kg m-2 5 0 0 20406080100 Years since slash or burn

Figure 10.2. Relation between total aboveground C stock (biomass and necromass) and time since last slash, burn or cultivation event; the slope indicates an average annual C -1 -1 stock increment of 2.5 Mg C ha year

10.5. References

Anderson J.M. and J.S.I. Ingram (eds.) (###) Tropical Soil Biology and Fertility, a Handbook of Methods. CAB International, Wallingford (UK) Brown, S., (1997). Estimating Biomass Change of Tropical Forest. FAO, Forestry Paper. FAO.USA. Hairiah, K., Cadisch, G. van Noordwijk, M., Latief, A.R.,Mahabharata and Syekhfani, (1995). Size-density and isotopic fractionation of soil organic matter after forest conversion. In: A. Schulte and D. Ruhiyat eds. Proc. Balikpapan Conf. on Forest Soils Vol. 2: 70-87. Mulawarman University Press, Samarinda, Indonesia Hairiah, K., Van Noordwijk, M. and Latief, A.R., (1996a). Soil organic matter fractionation under different land use types in N. Lampung. AGRIVITA 19: 146-149. Hairiah, K., Kasniari, D.N., Van Noordwijk, M. and de Foresta, H. and Syekhfani M.S., (1996b). Soil properties, litterfall, above-and belowground biomass during a Chromolaena odorata fallow. AGRIVITA 19: 184-192. Meijboom F.W., Hassink J. and Van Noordwijk, M., 1995 Density fractionation of soil macroorganic matter using silica suspensions. Soil Biology and Biochemistry 27: 1109- 1111. Palm, C, Hairiah, K. and Van Noordwijk, M., (1994). Methods for sampling above and belowground organic pools for ASB sites. in: D.M. Murdiyarso, K. Hairiah and M. Van Noordwijk (Eds.) Modelling and Measuring Soil Organic Matter Dynamics and Greenhouse Gas Emissions after Forest Conversion. Proceedings of Workshop/ Training Course 8-15 August 1994, Bogor/Muara Tebo. ASB-Indonesia publication No. 1. pp 57-71 Van Noordwijk, M., Tomich, T.P., Winahyu, R., Murdiyarso, D., Partoharjono, S. and Fagi, A.M. (Eds.) (1995). Alternatives to Slash-and-Burn in Indonesia, Summary Report of Phase 1. ASB-Indonesia Report Number 4, Bogor, Indonesia

153 Van Noordwijk, M., Woomer P., Cerri C., Bernoux, M. and Nugroho, K., (1997a). Soil carbon in the humid tropical forest zone. Geoderma 79: 187-225 Van Noordwijk, M, T.P. Tomich, D.P. Garrity and A.M. Fagi (Eds.), (1997b). Alternatives to Slash-and-Burn Research in Indonesia. ASB-Indonesia Report Number 6. Agency for Agricultural Research and Development, Bogor, Indonesia (ISBN 979-8161-59-9) Van Noordwijk, M., D. Murdiyarso, K. Hairiah, U.R. Wasrin, A. Rachman and T.P. Tomich, (1998). Forest soils under alternatives to slash-and-burn agriculture in Sumatra, Indonesia. In: A. Schulte and D. Ruhiyat (eds.) Soils of Tropical Forest Ecosystems: Characteristics, Ecology and management. pp. 175-185. Springer-Verlag, Berlin. Van Noordwijk, M., Hairiah, K., Woomer, P.L. and Murdiyarso, D. (1999) Criteria and indicators of forest soils used for slash-and-burn agriculture and alternative land uses in Indonesia. in: E. Davidson (Ed.) ASA publication in press Woomer, P.L., Palm, C.A., Alegre, J., Castilla, C., Cordeiro, D.G., Hairiah, K., Kotto-Same, J., Moukam, A., Ricse, A., Rodriguez, V. and Van Noordwijk, M., 1998?. Carbon dynamics in slash-and-burn systems and land use alternatives. In: .(Eds.) .... in press

154 SECTION 11: PRELIMINARY SYNTHESIS: SUMMARY OF PLANT-BASED INDICATORS OF BIODIVERSITY AND SOIL NUTRIENT AVAILABILITY

By A.N. Gillison

11.1 Introduction

At the time of writing newly acquired insect taxonomic and beetle trophic data have just come to hand. These will be added to the present data pool and the whole re-analysed to seek new and informative relationships between plant and animal taxa, functional types, and site physical variables. All the data sets thus far have been subjected to correlative analysis to identify those plant-based variables with highest predictive values along the land use intensity gradient. Because biodiversity is made up of interacting complexes of taxa, functional types, individuals and populations, correlation analysis is an appropriate first point of entry in seeking efficient predictors. High correlations do not necessarily indicate causal relationships although in some cases this can be reasonably inferred, especially where correlates bewteen different sets of variables vary consistently with soil nutrient availability.

11.2 Methods

Standard methods of regression (Pearson product-moment, using the MINITAB statistical package) were applied to help identify potentially useful plant-based surrogates of biodiversity. The sets of variables with highest linear correlates were also examined for non-linear pattern (second order polynomial regression as described in Part B, Annex II.) Because some of the highest correlates were associated with richness in plant species, modi and a ratio of species:modi, these variables were also examined for their potential value in predicting the occurrence of other taxa and functional groups as well as site physical variables. Table 11.1 below outlines the results.

11.3 Results

Plant species and PFTs or modi used alone and as a ratio tend to account for more variance in richness in animal taxa than animal taxa themselves. Overall there is a tendency for the species/modi ratio to greatly improve prediction for certain taxa and for above-ground carbon. This is evident in Table 11.1 a,b,c,d and is further illustrated in Annex II Figs 1a,b,c,d. for above-ground carbon, collembola, birds and termites. Table 11.2 lists correlations between plant-based variables and soil physico-chemical attributes.

11.4 Discussion

When the pattern of plant and animal taxonomic distribution along the LUTs is examined overall it is clear that the highest values tend to occur in certain pristine forest types and jungle rubber. This may be partly explained by the nature of the available niches in both intact forest and jungle rubber. Whereas the former has allowed the development of a series of cryptic terrestrial and arboreal habitats through a longer timespan, the relatively recent and more dynamic jungle rubber displays a much wider variety of niches due to the micro-fragmentary nature of the stand that is maintained by frequent disturbance by humans and animals. The cumulative graphs shown in Part B, Figure1.1 show considerable similarity between the area curves for BS05, the richest intact rain forest, and those for jungle rubber (BS10). The nature

155 of the curves for the ratio values also relfects closely the general dynamic status of the LUT and whether it is degraded or not. The integration of the curves may provide a useful means for developing an index that reflects the overall indicator value of the species, modi and ratio combinations and this is the focus of a continuing study.

Of particular significance are the non-linear relationships of certain plant and vegetation structural variables with a sub-set of insect and bird taxa where the correlation is dramatically improved with ratios of species to PFT richness as distinct from correlations with either species or PFTs alone (Part B, Annex II, Fig. 1a,b,c,d) . There is no immediate explanation for this improvement although there is some suggestion of covariant patterns in soil nutrient availability. Table 11.2 outlines correlations between plant-based variables and a range of soil physico-chemical attributes. There are clear correlations between certain physical variables such as soil bulk density, soil pH, organic carbon and total N and Aluminium and species and PFT richness and increasing complexity in vegetation structure. The vegetation “V” index (ref: Part B) is also highly correlated with a range of soil variables. While diversity indices are rarely attributed much significance as biodiversity indicators, in the present study each of the Shannon-Wiener, Simpson’s and Fisher’s Alpha values are significantly correlated with a variety of key soil nutrients (Table 11.2).

11.5 Conclusions and recommendations

Before an appropriate synthesis can be made further investigation of more recently acquired data must be undertaken. However it is safe to say that the study so far clearly indicates that assessments of biodiversity should be designed to sample as much as possible of the spatial ranges of the taxa of concern to management. In general this translates to seeking out representative gradients of land use intensity and soil nutrient availability at landscape level and including climate at ecoregional level. Once these system boundaries have been located they offer a useful spatial and environmental context for identifying, calibrating and testing by spatial extrapolation, the best sets of biodiversity indicators. It is clear that there will be many situations where isolated samples taken for example from rain forest alone, are likely to give a misleading picture of regional patterns of biodiversity. The present study has revealed a new set of indicators that show promise for much wider application and testing. The study has shown that the plant-based attributes with taxonomic + functional complements possess potentially useful predictive value when coupled with certain taxa, soil nutrients and above-ground carbon. Taken together with readily measureable elements of vegetation structure such as mean canopy height and basal area, these offer an exciting prospect for examining the dynamics of biodiversity and associated land use at landscape scale.

Based on present findings, the message for managers of forested and agroforested lands is to maintain a mosaic of land cover types to maximise the availability of ecological niches. Not only is this likely to enhance biodiversity, recent experience suggests this may have a beneficial effect by facilitating biological pest management as well as providing increased flexibility for varying management options under conditions of environmental and socioeconomic change.

156 Table 11.1a Linear correlations between beetle trophic groups#, plant species and PFTs*

Troph.Grp PFT Species Spp/PFT Pchew1sp 0.498 0.663 0.564 Pchew2sp 0.581 0.585 0.369 Pchewspt 0.539 0.650 0.508 Pchew1fm 0.349 0.663 0.713 Pchew2fm 0.764 0.590 0.190 Pchewfmt 0.431 0.713 0.711 Pred1sp 0.336 0.438 0.366 Pred2sp 0.322 0.491 0.443 Predsptt 0.354 0.467 0.394 Pred1fm 0.608 0.449 0.115 Pred2fm 0.044 0.324 0.448 Predfmtt 0.542 0.521 0.286 Scav1sp 0.411 0.404 0.245 Scav2sp 0.407 0.755 0.781 Scavsptt 0.437 0.503 0.372 Scav1fm 0.165 0.362 0.395 Scav2fm 0.338 0.794 0.928 Scavfmtt 0.210 0.471 0.525 Tot1sp 0.447 0.527 0.400 Tot2sp 0.583 0.744 0.608 Utotalsp 0.482 0.580 0.451 Totalsp 0.401 0.337 0.156 Tot1fm 0.335 0.577 0.572 Tot2fm 0.450 0.854 0.899 Totfam 0.339 0.573 0.562

# Pchew1sp = Phytophagous chewers – primary species; Pchew2sp= secondary species; Pchewt = total; -fm = family; Pred = predators; Scav = scavenger; Utotalsp = total unique species.

*PFTs = Plant Functional Types; Shaded areas with r = >0.500. Bold type = high indicator value.

157

Table 11.1b Linear correlations between beetle trophic groups and vegetation structure*

Troph.Grp# Can. Ht Cr. Cov% W. Plts Ba. Area Pchew1sp 0.313 0.172 0.734 0.388 Pchew2sp 0.248 0.232 0.529 0.329 Pchewspt 0.298 0.197 0.679 0.376 Pchew1fm 0.343 0.330 0.748 0.552 Pchew2fm 0.600 0.572 0.222 0.530 Pchewfmt 0.405 0.390 0.749 0.599 Pred1sp 0.104 0.126 0.631 0.248 Pred2sp 0.061 0.074 0.559 0.352 Predspt 0.106 0.128 0.661 0.272 Pred1fm 0.203 0.041 0.516 0.182 Pred2fm -0.090 -0.069 0.520 0.281 Predfmt 0.138 0.007 0.660 0.273 Scav1sp -0.005 0.142 0.461 0.203 Scav2sp 0.440 0.564 0.742 0.687 Scavspt 0.088 0.239 0.550 0.317 Scav1fm -0.044 0.117 0.538 0.281 Scav2fm 0.537 0.490 0.731 0.784 Scavfmt 0.063 0.196 0.614 0.397 Tot1sp 0.143 0.167 0.609 0.299 Tot2sp 0.359 0.402 0.705 0.536 Utotalsp 0.192 0.221 0.636 0.355 Totalsp -0.065 -0.008 0.478 0.103 Total1fm 0.232 0.276 0.680 0.473 Total2fm 0.572 0.549 0.712 0.846 Totalfm 0.223 0.264 0.682 0.465

* Can.Ht = Mean canopy height (m) Cr.Cov.% = Crown cover percent of dominant stratum W.Plts = Domin cover-abundance estimate of woody plants <2m tall B.Area = Basal area of all woody plants m2ha-1 (Bitterlich) Shaded areas with r = >0.500. Bold type = high indicator value.

158

Table 11.1c Linear correlations between richness of plant species, plant functional types and their ratios, and various animal taxa and above-ground plant carbon #

Attribute Species Modi Spp/Modi Ground-dwelling Termite abundance 0.872 0.766 0.946 Termite species 0.849 0.698 0.976 Lep/ground 0.834 0.790 0.920 Canopy: Unident. insects 0.771 0.418 0.839 Collembola 0.643 0.089 0.882 Ant-total 0.633 0.729 0.393 Total insects 0.593 0.487 0.526 Orthoptera 0.545 0.378 0.528 Thysanoptera 0.470 0.756 0.138 Isoptera (canopy) 0.417 0.140 0.496 Psocoptera 0.398 0.148 0.457 Coleoptera 0.312 0.458 0.127 Hymenoptera 0.302 0.446 0.129 Formicidae 0.274 0.370 0.142 Acari 0.190 -0.232 0.443 Spiders 0.186 0.307 0.050 Blattodea 0.124 -0.014 0.204 Hemiptera 0.098 0.229 -0.026 Diptera 0.038 0.404 -0.197 Bird total spp. 0.599 0.347 0.704 Above-ground 0.796 0.558 0.909 carbon

# Shaded areas with r = >0.500. Bold type = high indicator value.

159 Table 11.2 Plant-based linear correlates with soil physico-chemical attributes

pH_H2O pH_KCl C_org, % N_tot,% K Na Mg Al ECEC Al_sat Bulk D.

Mean Ht -0.719 -0.828 0.486 0.386 0.005 -0.205 -0.370 0.632 0.441 0.558 -0.770 0.002 0.000 0.056 0.140 0.984 0.446 0.159 0.009 0.087 0.025 0.000 Basal A. -0.684 -0.780 0.503 0.395 0.048 -0.198 -0.347 0.684 0.491 0.595 -0.784 0.004 0.000 0.047 0.130 0.859 0.462 0.188 0.003 0.053 0.015 0.000 CC% 0.215 0.125 0.092 0.095 -0.063 0.076 0.278 -0.057 -0.107 -0.089 -0.120 0.424 0.644 0.737 0.728 0.818 0.779 0.298 0.833 0.694 0.743 0.659 Wplts -0.285 -0.206 0.502 0.376 0.475 0.381 0.300 0.296 0.512 0.137 -0.627 0.284 0.445 0.048 0.151 0.063 0.146 0.259 0.265 0.043 0.614 0.009 Bryo -0.593 -0.777 0.459 0.526 0.097 -0.164 -0.300 0.697 0.584 0.527 -0.743 0.016 0.000 0.074 0.037 0.720 0.545 0.260 0.003 0.018 0.036 0.001 Mean FI 0.172 0.293 -0.144 -0.026 0.093 0.175 0.180 -0.123 -0.094 -0.074 0.291 0.525 0.270 0.594 0.925 0.732 0.516 0.504 0.651 0.728 0.786 0.274 Modi -0.402 -0.471 0.878 0.742 0.609 0.393 0.097 0.643 0.880 0.279 -0.890 0.123 0.066 0.000 0.001 0.012 0.132 0.720 0.007 0.000 0.295 0.000 Species -0.550 -0.653 0.716 0.550 0.329 0.104 -0.225 0.687 0.650 0.484 -0.868 0.027 0.006 0.002 0.027 0.214 0.700 0.403 0.003 0.006 0.058 0.000 Spp/modi -0.683 -0.745 0.405 0.278 -0.012 -0.196 -0.463 0.616 0.353 0.602 -0.742 0.004 0.001 0.120 0.298 0.966 0.466 0.071 0.011 0.180 0.014 0.001 Vindex 0.664 0.755 -0.611 -0.477 -0.174 0.056 0.291 -0.688 -0.575 -0.544 0.852 0.005 0.001 0.012 0.061 0.520 0.838 0.274 0.003 0.020 0.029 0.000 Shannon 0.352 0.231 -0.507 -0.496 -0.545 -0.327 -0.348 -0.366 -0.732 -0.049 0.615 0.181 0.390 0.045 0.051 0.029 0.217 0.186 0.163 0.001 0.858 0.011 Simpson -0.367 -0.309 0.722 0.661 0.647 0.445 0.327 0.479 0.866 0.100 -0.767 0.162 0.244 0.002 0.005 0.007 0.084 0.216 0.060 0.000 0.712 0.001 F_Alpha 0.488 0.542 0.240 0.174 0.585 0.633 0.876 -0.348 0.290 -0.651 -0.018 0.055 0.030 0.370 0.519 0.017 0.009 0.000 0.187 0.276 0.006 0.946

Refer Section 10 and Annex III Table 2 for soil symbols; Mean Ht = mean canopy height, Basal A = basal area m2 ha-1, CC% = crown cover percent, Wplts = Cover abundance of woody plants <1.5m tall, Bryo = cover abundance of bryophytes, Mean FI = Mean Furcation Index canopy trees, Modi = total functional modi or Plant Functional Types, Species = total plant species, Vindex = Vegetation Index, Shannon = Shannon-Wiener Diversity Index for PFTs, Simpson = Simpson’s diversity index for PFTs, F_Alpha = Fisher’s Alpha diversity index for PFTs. Correlation ’ r’ value on each first line, probability value on each second line; shaded cells with p <0.020. Clay not listed due to poor correlation.

160

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ABOVE-GROUND BIODIVERSITY ASSESSMENT WORKING GROUP SUMMARY REPORT 1996-99

Impact of different land uses on biodiversity 1 Compiled by A.N. Gillison (Coordinator)

Annexes I-V2

Gillison, A.N. (Coordinator), 2000. Above ground biodiversity assessment working group summary report 1996-99: Impact of different land uses on biodiversity and social indicators. Annexes I-V. ASB Working Group Report, ICRAF, Nairobi, 83p. [on-line] URL: http://www.asb.cgiar.org/PDFwebdocs/ASB Biodiversity Report Annexes.pdf

Editor: Polly Ericksen Cover Design: Damary Odanga and Bainitus Alenga Text Layout: Joyce Kasyoki Printers: Signal Press Cover photo: Debra Lodoen

Printed August, 2000.

Figure 1. Habitat profiles using cumulative Species, Modi and Species / modi ratio under different land use types

1. Intact rain Forest

Species Modi Spp/Modi

BS05 - Intact rain forest 120 60 3.0

100 50 2.5

80 40 2.0 60 30 1.5 40 20

1.0 20 10 0 0 0.5 0 10203040 0 10203040 5 10152025303540

Png2 - Intact rain forest 120 60 3.0

100 50 2.5 80 40 2.0 60 30

1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Bra24 - Intact forest/ 120 white sand 60 3.0 100 50 2.5

80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

1 ANNEX I Figure 1. Continue …

2. Secondary forest

Species Modi Spp/Modi

BS03- 120 60 3.0 Secondary forest 100 50 (log dump) 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Cam1-Secondary forest 120 60 3.0

100 50 2.5

80 40 2.0

60 30 1.5 40 20 1.0 20 10 0 0 0.5 0 10203040 0 10203040 5 10152025303540

120 Png1- 60 3.0 Secondary forest (‘85) 100 50 2.5 80 40 2.0 60 30 1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

120 Kny1-S.decid. 60 3.0 forest 100 50 2.5 80 40 2.0

60 30

1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

ANNEX I

Figure 1. Continue …

3. Savannas

Species Modi Spp/Modi

Bra22-Cerrado 120 60 3.0 (woodland savanna) 100 50 2.5

80 40 2.0 60 30

1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Cam17- Hyparrhenia 120 woodland,Savanna 60 3.0 100 50 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Mad2- Palm savanna 120 60 3.0

100 50 2.5

80 40 2.0 60 30 1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

BS12-Imperata savanna 120 60 3.0

100 50 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

ANNEX I

Figure 1. Continue … 4. Agroforests

Species Modi Spp/Modi

BS10-Jungle rubber 120 60 3.0

100 50 2.5

80 40 2.0 60 30

1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Cam10-Jungle 120 Cocoa 60 3.0 100 50 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

ANNEX I Figure 1. Continue …

5. Plantation

Species Modi Spp/Modi

BS8-Rubber plantation 120 60 3.0

100 50 2.5

80 40 2.0 60 30 1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

BS7-Paraserianthes 120 60 3.0 plantation 100 50 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540 Cam15-Cocoa plantation 120 60 3.0

100 50 2.5

80 40 2.0

60 30

1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

Png4-Oil palm plantation 120 60 3.0

100 50 2.5 80 40 2.0 60 30

1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 0 10203040

Bra5-Oil palm plantation 120 60 3.0 100 50 2.5

80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

ANNEX I

Figure 1. Continue …

6. Cassava

Species Modi Spp/Modi

120 BS14- 60 3.0 Cassava 3 yr 100 50 2.5

80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

120 Cam16- 60 3.0 Cassava 2yr 100 50 2.5

80 40 2.0 60 30 1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

120 Cam3- 60 3.0 New garden 100 50 2.5 80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

6 ANNEX I

Figure 1. Continue …

7. Fallow

Species Modi Spp/Modi

Cam2- 60 3.0 100 Chromolaena 2yr 90 50 2.5 80 70 40 2.0 60 30 50

40 1.5 20 30

20 1.0 10 10 0 0 0.5 0 10203040 0 10203040 5 10152025303540

BS16-Chromolaena 120 60 3.0

100 50 2.5 80 40 2.0 60 30

1.5 40 20 1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

120 Cam06- 60 3.0 Chromolaena 4yr 100 50 2.5

80 40 2.0 60 30 1.5 40 20

1.0 20 10

0 0 0.5 0 10203040 0 10203040 5 10152025303540

7 ANNEX I

Table 1. Site description for studies of patterns of richness in vascular plant species and plant functional types (MODI) along a gradient of land use types in lowland, tropical forested lands.

Land Use Type Georeference Plot Title Description

1. Intact Rain Forest 01o 04’ 55” S BS05 : Closed rain forest Unlogged, pristine lowland tropical rain forest, Pasir Mayang, Jambi, 102o 06’ 05” E Sumatra, Jambi Biodiversity Baseline study plot

05o 38’ 46” S PNG02 : Closed rain forest Unlogged foothill forest near Kimbe, West New Britain, Papua New 150o 06’ 14” E Guinea.

02o 35’ 21” N Bra24 : Campina forest on white Low forest on very low nutrient sands near Manaus, Brazil. 60o 01’ 55” E sand

2. Secondary Forest 01o 04’ 43” S BS03 : Secondary forest Old log dump within logged-over forest. very patchy, impacted soil. Jambi 102o 05’ 55” E Baseline Study plot, Pasir Mayang

03o 36’ 05” N Cam01 : Secondary forest Logged over 15 years ago.Awae village, Cameroon.ASB plot 11o 36’ 15” E

05o 31’ 23” S PNG01: Secondary forest Logged over 13 years ago. Kimbe district, West New Britain, Papua New 150o 04’ 32” E Guinea.

04o 11’ 23” N Kny01: S. Decid. forest Logged over, semi-deciduous forest, Shimba game reserve, near 39o 25’ 34” E Mombasa, Kenya. Elephant grazing.

3. Savannas 01o 35’ 58” S BS12 : Imperata savanna Imperata savanna (6 years old after Cassava), Kuamang Kuning, Jambi 102o 21’ 11” E Baseline Study plot.

8 ANNEX I

Table 1. Site description for studies of patterns of richness in vascular plant species and plant functional types (MODI) along a gradient of land use types in lowland, tropical forested lands.

Georeference Plot Title Description

05o 02’ 40” N Cam17 : Hyparrhenia, wooodland Stable savanna, Makam III, Cameroon. ASB plot. 10o 42’ 04” E savanna

18o 48’ 26” N Mad02 : Palm savanna Ravenala madagascariensis. dominated degraded, savanna, Saham 48o 43’ 29’’ E Pinga, Madagascar

38o 53’ 29” N SIMAB04 : Savanna Savanna 'meadow', Smithsonian research station, Front Royal Virginia, 78o 09’ 05” W USA.

** Bra22 : Cerrado woodland Cerrado, seasonal woodland savanna; Jardin Botanico, near Brasilia, savanna Brasil

4. Agroforests 01o 10’ 12” S BS10 : Jungle rubber Rubber trees grown in forest environment (14 years), Jambi Baseline 102o 06’ 50” E study plot

02o 34’ 37” N Cam10 : Jungle Cocoa Cacao plantation grown in forest environment. Mengomo, Ebolowa 11o 01’ 29” E station, Cameroon ASB plot.

5. Plantations 01o 05’ 25” S BS08 : Rubber plantation Rubber (Hevea) plantation (8yr). Pasir Mayang, Jambi Baseline Study 102o 07’ 05” E plot.

01o 03’ 09” S BS07: Paraserianthes softwood Softwood plantation (3yr). Pasir Mayang (Barito Pacific logging 102o 08’ 10” E plantation concession). Jambi Baseline Study plot.

9 ANNEX I

Table 1. Site description for studies of patterns of richness in vascular plant species and plant functional types (MODI) along a gradient of land use types in lowland, tropical forested lands.

Land Use Type Georeference Plot Title Description

05o 26’ 40” S PNG04 : Oil Palm plantation Oil palm plantation (18yr). Walindi, Kimbe district, West New Britain, 150o 05’ 07” E Papua New Guinea.

02o 53’ 34” S Bra25 : Oil Palm plantation Oil palm plantation (?16yr). EMBRAPA CPAF Research Station, Manaus, 59o 58’ 21” W Brazil.

02o 43’ 12” N Cam15 : Cacao plantation Cacao plantation (30yr). Akok village, Cameroon ASB plot. 11o 16’ 58” E

6. Subsistence garden 03o 36’ 05” N Cam03 : New garden New subsistence garden (groundnut, Cassava etc) Awae village, 11o 36’ 15” E Cameroon. ASB plot

01o 36’ 05” S BS14 : Cassava Cassava garden (?>3 yr), Kuamang Kuning village, Jambi Baseline Study 102o 21’ 22”’ E plot

04o 48’ 58” N Cam16 : Cassava Cassava garden (2yr). Bafia (savanna). Cameroon. ASB plot 11o 10’ 27” E

7. Fallows 03o 36’ 05” N Cam02 : Chromolaena Chromolaena dominated fallow (2 yr). Awae village, Cameroon. ASB plot 11o 36’ 15” E

01o 10’ 13” S BS16 : Chromolaena Chromolaena and Clibadium dominated 3 year fallow. Pancoran Gading 102o 06’ 58” E village. Jambi Baseline Study plot

03o 55’ 31” N Cam06 : Chromolaena Chromolaena (4 yr). Nkolfulu. Cameroon. ASB plot 11o 35’ 49” E

10 ANNEX I

Figure 2 Above-ground Carbon and Biodiversity All benchmark sites (ASB Phase II Report, 1998)

3.00

Primary 2.50 Forest

2.00 Managed Fallow Forest 1.50 Tree-based

1.00 Crop Plant Biodiversity Index Biodiversity Plant Pasture 0.50 0 100 200 300 400

Aboveground - C t/ha

Figure 3 Patterns of richness in plant species and functional types under different land use systems Indonesia : Jambi - Lampung

Legend

160 Cassava Imperata 140 Mono. Plantation Natural Forest 120 Logged Forest 100 Agroforestry

Species 60

0 102030405060

FunctionalModi Types (modi)

11 ANNEX I

Figure 4

Land Use Types ranked against “V” Index (from: veg.structure, species and functional types) Cameroon, Mbalmayo

1.0 F R ry 0.9 2 m a al av P s ff. as a C 0.8 R ld oa O oc C J. 0.7

F R 0.6 ry 2 om hr C yr or en 2 F d m 0.5 x. ar ro e G h va r C a m 1y yr s o 8 as hr na “V” Index “V” C C an 0.4 yr v PL 2 Sa a m o ro oc h C r C 0.3 4y na an av S m 0.2 ro h en C rd yr a 4 G 0.1 ew N

0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Land Use Type

Figure 5

Land Use Types ranked against “V” Index (from: veg.structure, species and functional types) Jambi, Sumatra, Indonesia R.F 1.0 R.F R.F 0.9 Jung.rub 0.8

0.7 Log.’83

0.6 Rub. plt Log.ramp 0.5

“ V” Index 0.4 Para.plt

0.3 Chrom. 0.2 Cassava 0.1 Imperata

0.0 1 2 3 4 5 6 7 8 9 10111213141516 Land Use Type

12 ANNEX I

Figure 6 Land Use Types ranked against “V” Index (from: veg.structure, species and functional types) Rondonia - Acré, Brazil

f. Lr st re fo ) ry p. 1.0 2 a (C st 0.9 re fo ry 2 O 0.8

t’ e f. 0.7 t B ro es Ag ‘B . mp o a 0.6 C fe of . C ra da 0.5 an B lt. p f. “V” Index ga of In C . 0.4 & lt. ff . p o b ia C ub s & R as b. 0.3 C ub R va n sa de as ar C g 0.2 ew N t. as t. P as ld P 0.1 O r. Imp

0.0 1 2 3 4 5 6 7 8 9 101112131415161718192021 Land Use Type

Figure 7

MDS of 27 Plant Functional Attributes over 9 LUTs LUT Dotted line is area of alternative ‘best bets’

1.5 Imperata (13) Imperata (12) Cassava (14) 1.0 Cassava (15) Paraserianthes (6) 0.5 LRFramp83 (3) Paraserianthes Chromolaena (7) (16) 0.0 Pltn Rubber (9)

ECTOR_2 Prim. RF

V (1) Pltn. Rubber -0.5 Prim. RF (8) (2) LRF83 LRF83 -1.0 (5) (4) Jungle Rubber (11) Jungle Rubber -1.5 (10)

-1.1 -0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 1.3 VECTOR_1

ANNEX I

Figure 8 Global environmental representativeness of 108 ASB sites in Western Amazon, Indonesia and Cameroon

W. Amazon Cameroon Central Sumatra DOMAIN similarity values > 95 90 – 95 85 – 90 80 – 85 Sample plots 75 – 80 > 75

(DOMAIN; 0.5 deg resolution) The DOMAIN similarity values based on Elevation; Potential evapotranspiration; Total annual precipitation; Precip driest month; Precipitation range; Minimum average monthly temperature; Maximum average monthly temperature.

Figure 9 Global environmental representativeness of 117 ASB sites in Western Amazon, Yucatan, Indonesia, plus Thailand and Madagascar

W. Amazon Cameroon DOMAIN similarity values Central Sumatra > 95 90 – 95 85 – 90 80 – 85

75 – 80 Sample plots > 75

(DOMAIN; 0.5 deg resolution) The DOMAIN similarity values based on Elevation; Evapotranspiration; Annual precipitation; Precip driest month; Mean temperature; Minimum average monthly temperature.

ANNEX I

Table 2. Sample Plot Location ASB Above Ground Biodiversity, INDONESIA (Jambi and Lampung)

No. Plot No Location Country Date Latitude Longitude Elev’n Vegetation

1 ASBJAM1 Pasir Mayang Biotrop site Indonesia 18/01/97 01 04 47 S 102 06 02 E 76 Intact Rain forest 2 ASBJAM2 Pasir Mayang Biotrop site Indonesia 18/01/97 01 04 53 S 102 06 09 E 76 LOA 79/80 (Secondary forest) 3 ASBJAM3 Pasir Mayang Biotrop site Indonesia 19/01/97 01 04 59 S 102 06 43 E 65 Paraserianthes plantation forest 4 ASBJAM4 Rantau Pandan (N. Park) Indonesia 23/03/97 01 40 01 S 101 56 04 E 150 Primary forest 5 ASBJAM5 Rantau Pandan Indonesia 23/03/97 01 39 47 S 101 56 34 E 140 Rubber jungle 6 ASBJAM6 Muara Kuamang Indonesia 20/01/97 01 39 47 S 101 56 34 E 140 Rubber forest 7 ASBJAM7 Muara Kuamang Indonesia 21/11/97 01 34 59 S 102 16 20 E 140 Secondary rain forest 8 ASBJAM8 Desa B. Harjo, Muara Kuamang Indonesia 13/11/97 01 35 30 S 102 18 28 E 140 Cassava plantation 9 ASBJAM9 Purwosari, Muara Bungo Indonesia 13/11/97 01 35 59 S 102 21 11 E 140 Imperata savanna 10 ASBJAM10 Purwosari, Muara Bungo Indonesia 13/11/97 01 35 55 S 102 21 11 E 140 Imperata grass land / alang (short) 11 ASBJAM11 Sungai Tilan, T.Tengah, B. Tebo Indonesia 13/11/97 01 33 15 S 102 23 26 E 140 Agroforestry rubber plantation 12 ASBJAM13 Sylvagama, Muara Tebo Indonesia 14/11/97 01 32 96 S 102 25 49 E 140 Secondary forest 13 ASBJAM14 Sylvagama, Muara Tebo Indonesia 14/11/97 01 36 22 S 102 20 54 E 140 Cassava plantation 14 ASBJAM15 Sylvagama, Muara Tebo Indonesia 14/11/97 01 31 51 S 102 22 37 E 140 Jungle Rubber 15 ASBLAM01 ICRAF site at BMSF Lampung Indonesia 13/09/97 04 30 32 S 104 55 29 E 100 Natural Forest 16 ASBLAM02 Negara Jaya, ICRAF site Indonesia 11/09/97 04 27 17 S 104 55 26 E 100 Cassava plantation 17 ASBLAM03 Negara Jaya, ICRAF site Indonesia 11/09/97 04 27 36 S 104 55 28 E 100 Imperata savanna 18 ASBLAM04 Negara Jaya, ICRAF site Indonesia 11/09/97 04 27 29 S 104 55 27 E 100 Oil palm 19 ASBLAM05 Kaliawi Indah, ICRAF site Indonesia 12/09/97 04 26 08 S 104 58 52 E 100 Secondary forest 20 ASBLAM06 Kaliawi Indah, ICRAF site Indonesia 12/09/97 04 26 08 S 104 58 52 E 100 Paraserianthes falcataria plantation 21 ASBLAM07 Tegal Mukti, ICRAF site Indonesia 12/09/97 04 27 21 S 105 00 53 E 100 Cassava plantation 22 ASBLAM08 Kaliawi Indah, ICRAF site Indonesia 12/09/97 04 26 42 S 104 59 27 E 100 Imperata savanna 23 ASBLAM09 Panaragan, ICRAF site Indonesia 12/09/97 04 29 08 S 105 02 11 E 100 Fruit Agroforestry 24 ASBLAM10 Panaragan, ICRAF site Indonesia 13/09/97 04 28 22 S 105 02 34 E 100 Cassava plantation 25 ASBLAM11 Panaragan Indah, ICRAF site Indonesia 13/09/97 04 27 43 S 105 02 00 E 100 Imperata

15 ANNEX 1

Table 2. Sample Plot Location ASB Above Ground Biodiversity, INDONESIA (Jambi and Lampung)

No. Plot No Location Country Date Latitude Longitude Elev’n Vegetation

26 ASBLAM12 Panaragan, ICRAF site Indonesia 12/09/97 04 27 38 S 105 01 46 E 100 Friut trees 27 ASBLAM13 Panaragan, ICRAF site Indonesia 12/09/97 04 28 22 S 015 03 13 E 100 Rubber, Banana 28 ASBLAM14 Panaragan, ICRAF site Indonesia 13/09/97 04 28 26 S 105 02 36 E 100 Secondary forest 29 ASBLAM15 Panaragan, ICRAF site Indonesia 12/09/97 04 28 33 S 105 03 50 E 100 Rubber plantation 30 ASBLAM16 Tegal Mukti, ICRAF site Indonesia 12/09/97 04 27 21 S 105 00 53 E 100 Imperata savanna 31 ASBJBS01 Pasir Mayang Indonesia 18/01/97 01 04 47 S 102 06 02 E 76 Intact rain forest 32 ASBJBS02 Pasir Mayang Indonesia 20/11/97 01 04 45 S 102 05 53 E 60 Intact rain forest 33 ASBJBS03 Pasir Mayang Indonesia 20/11/97 01 04 43 S 102 05 55 E 85 Secondary after logging 1984 36 ASBJBS04 Pasir Mayang Indonesia 18/01/97 01 04 53 S 102 06 09 E 90 LOA 79/80 (Secondary forest) 37 ASBJBS05 Pasir Mayang Indonesia 26/11/97 01 04 56 S 102 06 05 E 75 Logged over forest 38 ASBJBS06 Pasir Mayang Indonesia 19/01/97 01 04 59 S 102 06 43 E 65 Paraserianthes plantation forest 39 ASBJBS07 Pasir Mayang Indonesia 21/11/97 01 03 09 S 102 08 10 E 55 Paraserianthes plantation (3.5 years) 40 ASBJBS08 Pasir Mayang Indonesia 27/11/97 01 05 25 S 102 07 05 E 53 Rubber plantation 41 ASBJBS09 Pasir Mayang Indonesia 27/11/97 01 05 27 S 102 06 56 E 53 Rubber plantation 42 ASBJBS10 Pancuran Gading Indonesia 22/11/97 01 10 12 S 102 06 50 E 30 Jungle rubber 43 ASBJBS11 Pancuran Gading Indonesia 25/11/97 01 10 13 S 102 06 46 E 30 Jungle rubber 44 ASBJBS12 Kuamang Kuning Indonesia 24/11/97 01 35 58 S 102 21 11 E 40 Imperata 45 ASBJBS13 Kuamang Kuning Indonesia 24/11/97 01 35 56 S 102 21 12 E 40 Imperata 46 ASBJBS14 Kuamang Kuning Indonesia 24/11/97 01 36 05 S 102 21 22 E 48 Cassava Plantation 47 ASBJBS15 Kuamang Kuning Indonesia 24/11/97 01 36 05 S 102 21 21 E 48 Cassava plantation 48 ASBJBS16 Pancuran Gading Indonesia 25/11/97 01 10 13 S 102 06 58 E 30 Chromolaena dibadium regrowth

16 ANNEX I

Table 2. Sample Plot Location ASB Above Ground Biodiversity, BRAZIL

No. Plot No Location Country Date Latitude Longitude Elev’n Vegetation

1 BRA001 Ji Parana, Rondonia Brazil 15/4/97 10-55-23 S 61-57-25 W 230 Rubber and Coffee plantation 2 BRA002 Ji Parana, Rondonia Brazil 15/4/97 10-55-23 S 61-57-25 W 230 Rubber and Coffee plantation 3 BRA003 Ji Parana, Rondonia Brazil 15/4/97 10-55-14 S 61-58-27 W 225 Brachyaria pasture 4 BRA004 Ji Parana, Rondonia Brazil 15/4/97 10-55-14 S 61-58-24 W 225 Brachyaria pasture 5 BRA005 Ji Parana, Sr J. do Cominhas Brazil 15/4/97 10-58-30 S 62-00-58 W 265 Plantation -agroforest (Schizolobium & Coffee) 6 BRA006 Ji Parana, Sr J. do Cominhas Brazil 15/4/97 10-58-30 S 62-00-58 W 265 Plantation - agroforest (Schizolobium & Coffee) 7 BRA007 Theobroma, Rondonia, Brazil 16/4/97 10-06-18 S 62-11-40 W 230 Cassava plantation 8 BRA008 Theobroma, Rondonia, Brazil 16/4/97 10-06-12 S 62-11-40 W 230 Inga edulis plantation 9 BRA009 Theobroma, Rondonia, Brazil 16/4/97 10-06-12 S 62-11-40 W 230 Cassia siamea plantation 10 BRA010 Theobroma, Rondonia Brazil 16/4/97 10-06-40 S 62-11-58 W 242 Rubber & Coffee plantation 11 BRA011 Theobroma, Rondonia Brazil 16/4/97 10-06-40 S 62-11-58 W 240 Rubber & Coffee plantation 12 BRA012 Theobroma, Rondonia Brazil 16/4/97 10-13-03 S 62-23-49 W 252 Disturbed vine forest 13 BRA013 Reca, Rondonia Brazil 17/4/97 09-46-48 S 66-37-44 W 287 Cupuacu and Bactris plantation 14 BRA014 Reca, Rondonia Brazil 17/4/97 09-46-48 S 66-37-44 W 287 Mixed agroforestry pltn; Cupuacu/Bactris/Bra. nut 15 BRA015 Reca, Rondonia Brazil 17/4/97 09-46-48 S 66-37-43 W 232 New subsistence garden (Slash & Burn) 16 BRA016 Reca, Rondonia Brazil 17/4/97 09-46-48 S 66-37-43 W 232 New subsistence garden, (Slash & Burn) 17 BRA017 Pedro Peixoto, Acre Brazil 18/4/97 10-01-13 S 67-09-39 W 271 Moderately disturbed rain forest 18 BRA018 Pedro Peixoto, Acre Brazil 18/4/97 10-01-13 S 67-09-39 W 295 Secondary forest - (Capoeira) 19 BRA019 Pedro Peixoto, Acre Brazil 18/4/97 10-01-13 S 67-09-39 W 295 Secondary forest - (Capoeira) 20 BRA020 Pedro Peixoto, Acre Brazil 18/4/97 10-01-03 S 67-09-27 W 316 Old Pasture 21 BRA021 Pedro Peixoto, Acre Brazil 18/4/97 10-01-03 S 67-09-27 W 316 Old pasture

17 ANNEX I

Table 2. Sample Plot Location ASB Above Ground Biodiversity, CAMEROON

No. Plot No Location Country Date Latitude Longitude Elev’n Vegetation

1 Camasb01 Cameroon, Awae village Cameroon 30/5/97 03-36-05 N 11-36-15 E 657 Secondary rain forest 2 Camasb02 Cameroon, Awae village Cameroon 30/5/97 03-36-05 N 11-36-15 E 657 Chromolaena fallow 3 Camasb03 Cameroon, Awae village Cameroon 30/5/97 03-36-05 N 11-36-15 E 657 New subsistence garden, slash & burn 4 Camasb04 Cameroon, Awae village Cameroon 30/5/97 03-36-05 N 11-36-15 E 657 8-10 Year Chromolaena fallow 5 Camasb05 Nkol Foulu village Cameroon 2/6/97 03-55-31 N 11-35-49 E 696 Secondary rain forest 6 Camasb06 Nkol Foulu village Cameroon 2/6/97 03-55-34 N 11-35-49 E 696 4 Year Chromolaena fallow 7 Camasb07 Nkol-fulu Mefou & Afamba Cameroon 2/6/97 03-55-34 N 11-35-49 W 696 New cultivation Egusi, Melon 8 Camasb08 Mengomo (Ebolowa-Station) Cameroon 2/6/97 02-34-45 S 07-02-05 W 554 Secondary forest 9 Camasb09 Mengomo (Ebolowo-Station) Cameroon 3/6/97 02-34-37 S 11-01-29 W 576 2 years Chromolaena fallow 10 Camasb10 Mengomo (Ebolowa-Station) Cameroon 3/6/97 02-34-37 S 11-01-29 W 576 Cocoa plantation non maintained 11 Camasb11 Akok (Ebolowa-Station) Cameroon 4/6/97 02-42-45 S 11-16-42 W 554 2 year Chromolaena fallow 12 Camasb12 Akok (Ebolowa-Station) Cameroon 4/6/97 02-42-27 S 11-16-90 W 554 1 year garden 13 Camasb13 Akok (Ebolowa-Station) Cameroon 4/6/97 02-43-08 S 11-17-05 W 585 Chromolaena fallow 14 Camasb14 Akok (Ebolowa-Station) Cameroon 4/6/97 02-43-12 S 11-16-58 W 585 2 year Chromolaena fallow 15 Camasb16 Bape (20km after BAFIA) Cameroon 5/6/97 04-48-58 S 11-10-27 W 560 1 year Cassava field 16 Camasb17 Makam III (Batoum II) Cameroon 5/6/97 05-02-40 S 10-42-04 W 977 humid savanna 17 Camasb18 Nkometou II Cameroon 5/6/97 04-04-51 S 11-33-17 W 596 1 year Chromolaena fallow 18 Camasb15 Akok (Ebolowa-Station) Cameroon 4/6/97 04-04-51 S 11-33-17 W 559 Cocoa Plantation 19 Camasb19 Bafia (Near Camasb16) Cameroon 27/8/96 04 48 56 N 11 10 25 E 640 Shrub savanna 20 Camasb20 Mbalmayo Bilik, Nkolitan Cameroon 28/8/96 03 28 21 N 11 29 25 E 635 Raffia palm swamp 21 Camasb21 Akok ‘Enuzam’ Cameroon 28/8/96 02 42 45 N 11 16 45 E 550 Old secondary forest

18 ANNEX I

Table 2. Sample Plot Location ASB Above Ground Biodiversity, PERU

No. Plot No Location Country Date Latitude Longitude Elev’n Vegetation

1 PUC001 Von Humboldt National Park Peru 22/4/97 08-48-01 S 75-03-54 W 246 Disturbed tropical lowland rain forest 2 PUC002 Umberto Romero, Km72, Pucallpa Peru 22/4/97 08-43-22 S 75-00-32 W 294 Improved pasture 3 PUC003 Campo Verde, Km 50 Cr Nueva Regina Peru 24/4/97 08-26-01 S 74-49-15 W 210 Secondary forest 4 PUC004 Campo Verde, Km 50 Cr Nueva Regina Peru 24/4/97 08-26-01 S 74-49-15 W 210 Secondary forest 5 PUC005 Sais Tupac, Km 10, Cr. Nueva Regina Peru 24/4/97 08-23-10 S 74-50-24 W 244 20 year old pasture 6 PUC006 Sais Tupac, Km 10, Cr. Nueva Regina Peru 24/4/97 08-23-10 S 74-50-24 w 244 Improved pasture 7 PUC007 Monte Los Olivos, Km 6 Cr. Curimana Peru 25/4/97 08-35-47 S 74-59-42 W 214 One month old maize garden 8 PUC008 Monte Los Olivos, Km 6 Cr. Curimana Peru 25/4/97 08-35-47 S 74-59-42 W 204 Maize garden 9 PUC009 Monte los Olivos Peru 25/4/97 08-35-43 S 74-59-42 W 204 Bachharis fallow 10 PUC010 Monte los Olivos Peru 25/4/97 08-35-47 S 74-59-42 W 244 Pueraria fallow 11 PUC011 Comite Palmeiras, Km46, Cr. Federico Basarde Peru 25/4/97 08-34-40 S 74-52-25 W 244 Oil palm (E. guineensis) plantation 12 PUC012 Comite Palmeiras, Km46, Cr. Federica Basarde Peru 25/4/97 08-34-39 S 74-52-22 W 242 Oil Palm Plantation 13 PUC013 Fundo Villa Delicia, Km42 Cr. Federico Basarde Peru 25/4/97 08-31-29 S 74-51-58 W 250 Degraded natural pasture 14 PUC014 Fundo Villa Delicia, Km42 Cr. Federico Basarde Peru 25/4/97 08-31-29 S 74-51-58 W 250 Natural pasture - degraded 15 PUC015 INIA Experimental Plots, Pucallpa Peru 23/4/97 08-23-26 S 74-33-31 W 198 Agroforestry plot, mixed species

19 ANNEX I

Table 2. Sample Plot Location ASB Above Ground Biodiversity, Yucatan, MEXICO

No. Plot No Location Country Date Latitude Longitude Elev Vegetation

1 YUCO01 Cafetal Limones, Yucatan Mexico 30/4/97 19-01-31 N 88-04-21 W 70 Secondary forest 2 YUCO02 Cafetal Limones, Yucatan Mexico 30/4/97 19-02-26 N 88-03-20 W 30 Secondary forest (logged) 3 YUCO03 Cafetal Limones, Yucatan Mexico 30/4/97 19-01-57 N 88-07-07 W 27 Citrus orchard 4 YUCO04 Laguna Cana, Yucatan Mexico 1/5/97 19-27-58 N 88-24-41 W 54 High forest (Bosque alta) 5 YUCO05 Carillo Porto, Yucatan Mexico 1/5/97 19-35-38 N 88-05-24 W 58 Secondary growth 1 year after garden 6 YUCO06 Ejido Nov 20, Yucatan Mexico 2/5/97 18-06-04 N 89-18-26 W 219 Citrus - Pimentos orchard 7 YUCO07 Ejido Nov 20, Yucatan Mexico 2/5/97 18-26-04 N 89-18-26 W 226 2 year regrowth after maize growth 8 YUCO08 Ejido Nov 20, Yucatan Mexico 2/5/97 18-26-12 N 89-18-18 W 224 Cow pasture 9 YUCO09 Ejido Nov 20, Yucatan Mexico 2/5/97 18-26-08 N 89-18-19 W 224 5 year regrowth

20 ANNEX I

Table 3. List of Participants

Dr. Douglas Sheil Dr. David E. Bignell Oxford University, Department of Plant Tropical Biology & Conservation Unit, Sciences, Oxford OX1 3RB, UK Universiti Malaysia Sabah, 88999 Kota Kinabalu, Tel: (01865) 275000; Fax: (01865) 275074 Sabah, Malaysia. Email: [email protected] Permanen address: New address: School of Biological Sciences, Queen Mary & CIFOR Westfield College, University of London, PO Box 6596, JKPWB, Jakarta 10065, London, UK E1 4NS INDONESIA Fax.: +44 181 9830973 Tel: (62) 251 622622; Fax: (62) 251 622100 Email: [email protected] or Email: [email protected] [email protected]

Dr. David T. Jones {PRIVATE}Dr. Allan D. Watt Biodiversity Division, Entomology Department Institute of Terrestrial Ecology; Edinburgh The Natural History Museum, Cromwell Road Research Station, Bush Estate, Penicuik, London, SW7 5BD, Midlothian EH26 0QB, Scotland, UNITED KINGDOM UNITED KINGDOM Tel: (1) 71 938 9442; Fax: (1) 71 938 8937 Tel: +44 131 445 4343; Fax: +44 131 445 3943 Email: [email protected] Email: [email protected] or [email protected] Web.: www.nmw.ac.uk/ite/edin/

Dr. Paul Jepson Mr. Paul Zborowski School of Geography, Mansfield Rd, PO Box 867 Kuranda Oxford, OX1 3TB, QLD 4872 UNITED KINGDOM AUSTRALIA Tel: +44 1865 271929; Fax: +441865 558637 Tel/Fax: +61 07 40930305 Email: [email protected] Email: [email protected]

Dr. Andy Gillison Dr. Meine van Noordwijk CIFOR ICRAF PO Box 6596, JKPWB, Jakarta 10065, PO Box 161, Bogor 16001, INDONESIA INDONESIA Tel: (62) 251 622622; Fax: (62) 251 622100 Tel: (62) 251 625415; Fax: (62) 251 625416 Email: [email protected] Email: [email protected]

Dr. Kurniatun Hairiah Dr. Suryo Hardiwibowo Brawijaya University Gadjah Mada University Faculty of Agriculture Faculty of Forestry Jl. Veteran, Malang 65145, Bulak Sumur, Yogyakarta, INDONESIA INDONESIA Tel: (62) 341564 355; Fax: (62) 341 564333 Tel: (62) 274 901400; Fax: (62) 274 901420 Email: [email protected] Email: [email protected]

Dr. F.X. Susilo Dr. Upik Rosalina Wasrin Lampung University SEAMEO-BIOTROP Faculty of Agriculture Jl. Raya Tajur Km 6 Jl. Prof. Dr. Sumantri, Bojonegoro No. 1 PO Box 17, Bogor Bandar Lampung 35145, INDONESIA INDONESIA Tel: (62) 251 371654 or 323 848 Tel: (62) 721 787029; Fax: (62) 721 702767 Fax: (62) 251 371 656 or 326 851 Email: [email protected] Email: [email protected]

21 ANNEX I

Table 3. List of Participants

Drs. Suhardjono Mrs. J.J. Afriastini Herbarium Bogoriense Herbarium Bogoriense Botanical Division, Research and Development Botanical Division, Research and Development Center for Biology-LIPI Center for Biology-LIPI Jl. Juanda 22, Bogor 16122, Jl. Juanda 22, Bogor 16122, INDONESIA INDONESIA Tel: (62) 251 322035 Fax: (62) 251 325854 Tel: (62) 251 322035 Fax: (62) 251 325854 Email: [email protected] Mr. M.H. Sinaga Ir. Ibnu Maryanto, MSc. Museum Zoology, Research and Development Museum Zoology, Research and Development Center for Biology LIPI, Center for Biology LIPI, Gedung widyia satwa loka balitbang zoology Gedung widyia satwa loka balitbang zoology puslitbang biology LIPI puslitbang biology LIPI Jl. Raya Bogor Jakarta km 46 Cibinong, Jl. Raya Bogor Jakarta km 46 Cibinong, INDONESIA INDONESIA Tel.: (62) 21 8765056-64; Fax: (62) 21 8765068 Tel.: (62) 21 8765056-64; Fax: (62) 21 8765068 Email: [email protected] Email: [email protected]

Ir. Edy Permana Ir. Iwan Setiawan Laboratory Assistant Remote Sensing & Ecology Laboratory Assistant Remote Sensing & Ecology Lab, SEAMEO-BIOTROP Lab, SEAMEO-BIOTROP Jl. Raya Tajur Km 6 Jl. Raya Tajur Km 6 PO Box 17, Bogor PO Box 17, Bogor INDONESIA INDONESIA Tel: (62) 251 371654 or 323 848 Tel: (62) 251 371 654 or 323 848 Fax: (62) 251 371 656 or 326 851 Fax: (62) 251 371 656 or 326 851 Email: [email protected] Email: [email protected]

Mrs. Oemiyati Rachmatsyah, MS Ir. Nining Liswanti Forestry Faculty of Bogor Agricultural Institute CIFOR (Forest Protection) PO Box 6596, JKPWB, Jakarta 10065, Darmaga Campus, Bogor INDONESIA INDONESIA Tel: (62) 251 622622 Fax: (62) 251 622100 Tel: (62) 251 627750 Fax: (62) 251 621256 Email: [email protected]

Mr. Djarwadi, MS Mr. Wardhana Forestry Faculty of Bogor Agricultural Institute Forestry Faculty of Bogor Agricultural Institute (Forest Conservation) (Forest Protection) Darmaga Campus, Bogor Darmaga Campus, Bogor INDONESIA INDONESIA Tel: (62) 251 621947 Fax: (62) 251 621256 Tel: (62) 251 627750 Fax: (62) 251 621256

Ms. C.H. Noor Rohmah Mr. Agus Kartono CIFOR Forestry Faculty of Bogor Agricultural Institute PO Box 6596, JKPWB, Jakarta 10065, Darmaga Campus, Bogor INDONESIA INDONESIA Tel: (62) 251 622622 Fax: (62) 251 622100 Tel: (62) 251 621947 Fax: (62) 251 621256

ANNEX II

Figures 1 a, b, c, d

Comparative correlations of plant species, functional types (modi) and species / modi with above ground carbon and three animal taxa

Figure 1a. Comparative relationships between above-ground carbon against: (A) Plant species richness, (B) Plant functional type richness, and (C) Species / modi ratios along a gradient of land use types, Jambi, Lowland Sumatra.

60 Y = -10.2048 + 1.10112X - 6.54E-03X**2

[r2 = 0.352]

0 m-2) (kg ground carbon Above -10 0 10 20 30 40 50 60 70 80 90 100 110 120

Plant species richness A

60 Y = -3.04936 + 0.207312X + 1.04E-03X**2 [r2 = 0.638] 50

Above ground carbon (kg m-2) (kg ground carbon Above -10

0 10 20 30 40 50

B Plant functional type richness

60 Y = 13.5654 - 23.5218X + 11.3031X**2 [r2 = 0.814] BS1 50 BS2 BS5 40

BS4

20 BS10 BS6 BS9 BS3 BS8 BS11 10 BS14,16 BS7 BS12

Above ground carbon (kg m-2) (kg ground carbon Above BS13 24 0 BS15

1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 C Species / Modi ratio

Figure 1b. Comparative relationship between Collembola species richness against: (A) Plant species richness, (B) Plant functional type richness, and (C) Species / modi ratios along a gradient of land use types, Jambi, Lowland Sumatra

Y= -3.05057 + 8.97E-02X-2.06E-04X**2 8 2 [r = 0.424] 7

4 3

Collembola species 1

40 50 60 70 80 90 100 110 120 A Plant species richness

Y= 0.641208 + 9.67E-02X-1.02E-03X**2 2 8 [r = 0.008]

4 3

Collembola species 1 0

15 25 35 45 55 Plant functional type richness B

Y= 8.07617-10.2473X+3.25155X**2 8 [r2 = 0.880] BS1 7

6 BS4 5 BS5 4 BS2 BS3 3 BS11 2

Collembola species BS9 BS10 1 BS7 BS6 25 0 BS8

1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

C Species / modi ratio

Figure 1c. Comparative relationship between Termite species richness against: (A) Plant species richness, (B) Plant functional types richness, and (C) Species / modi ratios along a gradient of land use types, Jambi, Lowland Sumatra

r- sq = 0.70

10 Termite species richness

10 20 30 40 50 60 70 80 90 100 110 120 A Plant species richness

40 r- sq = 0.47

Termite species richness 0

10 20 30 40 50 B Plant functional type richness

r- sq = 0.97 BS1 30

BS3 BS10 20

BS8 BS6 10 BS12 Termite species richness 26 BS14 0

1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

C Species / modi ratio

Figure 1d. Comparative relationship between Bird species richness against: (A) Species richness, (B) Plant functional type richness, and (C) Species / modi ratio along a gradient of land use types, Jambi, Lowland Sumatra

60 Y= 26.0471+1.32E-02X+1.10E-03X**2 [r2 = 0.344]

Bird species richness 20

10 0 10 20 30 40 50 60 70 80 90 100 110 120 A Plant species richness

60 Y= 23.7197+0.570855X-7.18E-03X**2 [r2 = 0.083]

30 Bird species richness 20

10 0 10 20 30 40 50

B Plant functional type richness

60 Y= 60.8406+44.3748X+13.5864X**2 [r2 = 0.690] BS5

50 BS3

BS4 40 BS2

BS16 BS10 BS1 30 BS6 BS12 BS13

Bird species richness BS7 20 27 BS8

10 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 C Species / modi ratio ANNEX III: Benchmark site data

Table 1a. Site locations and physical features for Jambi Benchmark sites

NO. SITE LOCALITY DATE OBSERVERS LAT LONG ELEV SLP ASP SO_DEP LTR PA_ROCK TERR SOIL TYPE

1 BS01 Pasir Mayang 1/18/97 ANG/NL/I 01-04-47 S 102-06-02 E 76 25 7 > 100 10 Sedimentary Upper slope Sandy loam 2 BS02 Pasir Mayang 11/20/97 AG/NL/AR/SH/UR/EP 01-04-45 S 102-05-53 E 60 36 115 > 100 10 Sedimentary Upper slope Ultisol 3 BS03 Pasir Mayang 11/20/97 AG/NL 01-04-43 S 102-05-55 E 85 12 150 > 100 15 Sedimentary Ridge top Ultisol 4 BS04 Pasir Mayang 1/18/97 ANG/NL/I 01-04-53 S 102-06-09 E 0 45 130 > 100 6 Sedimentary Mid slope Clay loam 5 BS05 Pasir Mayang 11/26/97 AG/NL/EP/AF/SH 01-04-56 S 102-06-05 E 75 25 75 > 100 8 Sedimentary Upper slope Ultisol 6 BS06 Pasir Mayang 1/19/97 ANG/NL/I 01-04-59 S 102-06-43 E 65 20 202 > 100 3 Sedimentary Upper slope Clay loam 7 BS07 Pasir Mayang 11/21/97 AG/EP/Tini/S 01-03-09 S 102-08-10 E 55 12 202 >100 6 Sedimentary Upper slope Ultisol 8 BS08 Pasir Mayang 11/27/97 AG/EP/AF/SH 01-05-25 S 102-07-05 E 53 3 183 > 100 5 Sedimentary Upper slope - ridge Ultisol 9 BS09 Pasir Mayang 11/27/97 AG/EP/AF/SH 01-05-27 S 102-06-56 E 53 3 188 > 100 5 Sedimentary Upper slope - ridge Ultisol 10 BS10 Pancuran Gading 11/22/97 AG/EP/Tini/S 01-10-12 S 102-06-50 E 30 0 0 >100 8 Sedimentary Flat Ultisol 11 BS11 Pancuran Gading 11/25/97 AG/NL/EP/AF/SH 01-10-13 S 102-06-46 E 30 0 0 >100 6 Sedimentary Flat Ultisol 12 BS12 Kuamang Kuning 11/24/97 AG/NL/EP/SH/Tini 01-35-58 S 102-21-11 E 40 5 225 >100 0.1 Sedimentary Ridge Ultisol 13 BS13 Kuamang Kuning 11/24/97 AG/NL/EP 01-35-56 S 102-21-12 E 40 5 130 > 100 0.1 Sedimentary Upper slope - ridge Ultisol 14 BS14 Kuamang Kuning 11/24/97 AG/NL/EP/AF/SH 01-36-05 S 102-21-22 E 48 0 0 > 100 0.5 Sedimentary Ridge Ultisol 15 BS15 Kuamang Kuning 11/24/97 AG/NL 01-36-05 S 102-21-21 E 48 9 311 > 100 0.2 Sedimentary Upper slope Ultisol 16 BS16 Pancuran Gading 11/25/97 AG/NL/EP/AF/SH 01-10-13 S 102-06-58 E 30 0 0 > 100 4 Sedimentary Flat Ultisol

LAT: Latitude; LONG: Longitude; ELEV: Elevation; SLP: Slope; ASP: Aspect; SO_DEP: Soil Depth; LTR: Litter; PA_ROCK: Parent Rock; TERR: Terrain Unit

28 ANNEX III

Table 1b. Site locations and physical features for Jambi Benchmark sites

No. Site Vegetation Mcan Ccov BA1 BA2 BA3 BA-AV BRY WP fi1 fi2 fi3 fi4 fi5 fi6 fi7 fi8 fi9 fi10 fi11 fi12 fi13 fi14 fi15 1 BS01 Intact rain forest 21 75 26 28 28 27.33 2 7 15 10 20 0 15 30 0 10 0 15 20 20 50 10 5 2 BS02 Intact rain forest 20 65 32 32 34 32.67 5 5 5 30 5 5 5 5 0 20 5 0 25 30 0 0 30 3 BS03 Secondary after logging 1984 10 35 10 12 18 13.33 3 6 5 10 0 0 60 90 0 0 0 0 0 0 0 0 0 4 BS04 LOA 79/80 (Secondary forest) 24 80 32 26 40 32.67 3 7 0 10 55 0 0 0 0 0 0 0 0 30 10 10 0 5 BS05 Logged over forest 28 70 28 24 30 27.33 4 6 20 30 30 20 5 0 0 0 0 0 5 20 30 5 10 6 BS06 Paraserianthes plantation forest 6 40 10 2 6 6.00 1 4 70 80 50 50 40 0 60 0 60 70 95 0 0 0 0 7 BS07 Paraserianthes plantation (3.5 years) 16 30 8 8 8 8.00 2 5 90 0 0 0 20 90 20 60 0 0 0 0 0 0 10 8 BS08 Rubber plantation 11 65 16 14 14 14.67 4 4 0 0 90 75 70 0 70 0 0 0 0 90 30 40 70 9 BS09 Rubber plantation 12 70 22 12 12 15.33 4 4 0 20 0 10 0 90 0 0 50 90 80 95 0 95 10 10 BS10 Jungle rubber 14 50 22 16 16 18.00 3 6 0 90 60 20 0 0 60 50 10 0 70 80 40 10 0 11 BS11 Jungle rubber 14 50 18 20 24 20.67 3 7 80 10 70 10 30 70 30 70 0 20 0 40 50 30 70 12 BS12 Imperata 1 90 0.01 0.01 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 BS13 Imperata 1 90 0.01 0.01 0.01 0.01 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 BS14 Cassava plantation 1.8 50 0.1 0.1 0.1 0.10 1 5 100100 95 100 100 100 95 95 100 100 95 100 100 100 100 15 BS15 Cassava plantation 1.8 40 0.1 0.1 0.1 0.10 1 4 95 100 100 90 100 95 100 100 100 100 100 95 95 100 100 16 BS16 Chromolaena dibadium regrowth 2 95 0.1 0.1 0.1 0.10 1 9 100100 10 20 100 100 100 100 100 100 100 100 0 100 0

Mcan: Mean canopy; Ccov: Crown cover; BA: Basal Area; BA_AV: Basal area average; BRY: Bryophyte; WP: Woody plants; Fi: Furcation Index

29 ANNEX III

Table 1b (cont.) Site locations and physical features for Jambi Benchmark sites

No. Site fi16 fi17 fi18 fi19 fi20 fi-av Remarks Location 1 BS01 15 0 10 5 20 13.50 Pasir Mayang. Outside Bl 8. Permanent plot Biotrop Pasir Mayang 2 BS02 60 60 5 0 20 15.50 Intact forest immediately adjacent to BIOTROP permanent plot reopened canopy possibly due to drought Pasir Mayang 3 BS03 0 5 5 10 20 10.25 Heavily logged over, includes snig-track Pasir Mayang 4 BS04 0 30 20 25 0 9.50 Pasir Mayang Biotrop Pasir Mayang 5 BS05 10 5 0 0 5 9.75 Logged over 1983, but very patchy. Approaching primary forest status. Replicate for BS4 Pasir Mayang 6 BS06 70 85 75 70 0 43.75 Pasir Mayang. Industrial forest plantation 1993/1994 Pasir Mayang 7 BS07 30 10 0 0 0 16.50 4 year old Paraserianthes falcataria plantation Pasir Mayang 8 BS08 60 95 95 0 0 39.25 Rubber plantation +/- 8 years Pasir Mayang 9 BS09 70 80 80 60 0 41.50 Rubber plantation +/- 8 years Pasir Mayang 10 BS10 100 90 0 0 90 38.50 15-38 year old Jungle Rubber - Hevea brasiliensis planted in among nature species Pancuran Gading 11 BS11 60 50 30 80 5 40.25 15-38 year Jungle Rubber - very disturbance Pancuran Gading 12 BS12 0 0 0 0 0 0.00 Short imperata "Alang-alang" grassland fired annually Kuamang Kuning 13 BS13 0 0 0 0 0 0.00 Short imperata "Alang-alang" grassland fired annually Kuamang Kuning 14 BS14 100 100 100 95 100 98.75 Cassava plantation (>10 years) Kuamang Kuning 15 BS15 100 100 95 95 100 98.00 Cassava plantation (Replicate for BS14) Kuamang Kuning 16 BS16 0 0 100 100 100 71.50 Chromolaena Pancuran Gading

30 ANNEX III

Table 2. Soil physico-chemical features for Jambi benchmark sites

No. LUT Depth, PH PH C N P K Na Ca Mg Al H Sand Silt Clay ECEC Al_sat C/N cm H2O KCl org, % tot,% bray2 ratio BS1 NF 0_5 4 3.5 4.01 0.28 10.19 0.16 0.34 1.65 0.41 4.19 1.16 62 24 14 7.91 53.0 14.3 BS1 NF 5_10 4.7 3.8 1.86 0.14 4.19 0.09 0.24 1.54 0.51 4.19 0.85 62 20 18 7.42 56.5 13.3 BS1 NF 10_20 4.9 3.9 1.2 0.09 2.09 0.08 0.22 1.54 0.1 3.59 0.89 62 20 18 6.42 55.9 13.3 BS1 NF 20_30 4.9 4 0.8 0.06 1.69 0.06 0.22 1.03 0.07 3.53 0.83 64 18 18 5.74 61.5 13.3 BS2 NF 0_5 4.2 3.5 3.21 0.19 9.19 0.19 0.31 1.54 0.62 3.71 1.27 67 22 11 7.64 48.6 16.9 BS2 NF 5_10 4.7 3.8 2.01 0.13 6.69 0.11 0.24 1.54 0.1 3.53 0.83 69 19 12 6.35 55.6 15.5 BS2 NF 10_20 4.8 3.7 1.61 0.12 2.69 0.11 0.23 3.61 1.03 3.17 0.93 66 17 17 9.08 34.9 13.4 BS2 NF 20_30 4.8 4 0.96 0.07 1.69 0.09 0.2 1.54 0.1 2.99 1.06 67 17 16 5.98 50.0 13.7 BS3 LOF 0_5 4.5 3.7 1.85 0.13 2.69 0.12 0.25 1.55 0.51 2.93 0.8 54 8 38 6.16 47.6 14.2 BS3 LOF 5_10 5.2 3.8 1.53 0.12 5.19 0.1 0.29 2.06 0.21 2.69 0.24 81 10 9 5.59 48.1 12.8 BS3 LOF 10_20 5 4 1.36 0.11 4.69 0.08 0.2 1.03 0.51 2.69 0.74 67 13 20 5.25 51.2 12.4 BS3 LOF 20_30 4.8 4 1.2 0.08 3.16 0.06 0.18 1.02 0.51 3.02 0.99 65 13 22 5.78 52.2 15.0 BS4 LOF 0_5 4.5 3.6 4.66 0.28 18.02 0.15 0.25 1.12 1.02 4.15 1.09 81 11 8 7.78 53.3 16.6 BS4 LOF 5_10 4 3.5 3.13 0.18 5.19 0.11 0.25 1.55 1.34 3.29 1.38 79 10 11 7.92 41.5 17.4 BS4 LOF 10_20 4.6 3.7 2.09 0.12 3.69 0.09 0.25 2.57 0.41 3.29 1.38 77 10 13 7.99 41.2 17.4 BS4 LOF 20_30 4.7 3.7 1.85 0.12 2.69 0.08 0.28 2.37 0.21 3.41 0.95 74 10 16 7.3 46.7 15.4 BS5 LOF 0_5 4.2 3.3 4.41 0.28 6.19 0.2 0.39 2.06 0.31 2.69 1.65 79 13 8 7.3 36.8 15.8 BS5 LOF 5_10 4.5 3.8 1.91 0.12 6.13 0.1 0.28 1.12 1.22 2.97 0.97 79 13 8 6.66 44.6 15.9 BS5 LOF 10_20 4.8 3.9 1.61 0.1 4.65 0.07 0.22 1.33 0.41 2.97 0.73 76 11 13 5.73 51.8 16.1 BS5 LOF 20_30 4.8 4 1.27 0.1 4.15 0.07 0.16 1.22 0.61 2.67 0.66 75 15 10 5.39 49.5 12.7 BS6 HTI 0_5 4.4 3.9 2.78 0.17 18.52 0.18 0.38 2.04 0.61 2.61 0.47 84 8 8 6.29 41.5 16.4 BS6 HTI 5_10 4.3 3.9 2.15 0.13 9.1 0.06 0.19 1.33 1.22 2.67 0.72 82 10 8 6.19 43.1 16.5 BS6 HTI 10_20 4.8 4 1.67 0.1 5.64 0.06 0.14 1.54 1.02 2.31 0.77 79 8 13 5.84 39.6 16.7 BS6 HTI 20_30 4.8 4.1 0.5 0.05 2.66 0.04 0.13 1.22 0.31 2.55 0.6 74 10 16 4.85 52.6 10.0 BS7 HTI 0_5 5.2 3.8 4.21 0.28 8.78 0.41 0.62 4.68 1.56 1.33 0.87 46 28 26 9.47 14.0 15.0 BS7 HTI 5_10 5.2 3.9 2.11 0.16 1.2 0.21 0.45 4.16 1.14 1.89 0.21 45 19 36 8.06 23.4 13.2 BS7 HTI 10_20 4.8 3.6 1.78 0.14 0.69 0.19 0.43 3.12 1.04 4.23 0.8 43 22 35 9.81 43.1 12.7 BS7 HTI 20_30 4.8 3.6 1.62 0.11 0.19 0.12 0.38 1.87 1.25 5.14 0.9 43 22 35 9.66 53.2 14.7 BS8 RUB_P 0_5 4.6 3.5 5.97 0.38 1.2 0.19 0.36 2.41 0.95 3.96 2.07 14 27 59 9.94 39.8 15.7 BS8 RUB_P 5_10 4.5 3.7 2.95 0.18 0.19 0.12 0.29 2.1 0.31 2.81 1.25 14 11 75 6.88 40.8 16.4 BS8 RUB_P 10_20 4.9 3.7 1.96 0.13 0.19 0.12 0.33 1.68 0.41 2.81 0.86 12 16 72 6.21 45.2 15.1

31 ANNEX III

Table 2. Soil physico-chemical features for Jambi benchmark sites

No. LUT Depth, PH PH C N P K Na Ca Mg Al H Sand Silt Clay ECEC Al_sat C/N cm H2O KCl org, % tot,% bray2 ratio BS8 RUB_P 20_30 4.9 3.8 1.86 0.12 0.19 0.1 0.32 1.52 0.94 1.63 0.71 11 13 76 5.22 31.2 15.5 BS9 RUB_P 0_5 4.4 3.6 3.27 0.53 10.04 0.27 0.38 1.78 0.59 5.67 1.89 15 41 44 9.4 60.3 6.2 BS9 RUB_P 5_10 4.8 3.7 2.41 0.31 7.5 0.13 0.36 1.62 0.42 3.23 1.21 13 15 72 7.65 42.2 7.8 BS9 RUB_P 10_20 4.7 3.9 2.19 0.16 1.25 0.09 0.18 1.8 1.08 3.14 1.04 13 18 69 7.5 41.9 13.7 BS9 RUB_P 20_30 4.5 3.9 2.13 0.14 0.18 0.05 0.17 1.57 0.63 3.36 1.08 12 23 65 6.82 49.3 15.2 BS10 J_RUB 0_5 5.2 3.8 6.23 0.46 41.51 0.51 0.69 2.37 0.76 5.31 2.63 6 70 24 10.72 49.5 13.5 BS10 J_RUB 5_10 5.1 3.8 3.97 0.28 17.18 0.23 0.63 2.12 0.42 5.05 1.49 7 58 35 11.08 45.6 14.2 BS10 J_RUB 10_20 5.1 3.8 2.81 0.22 10.49 0.22 0.37 1.59 0.21 4.93 1.48 5 54 41 8.81 56.0 12.8 BS10 J_RUB 20_30 5.1 3.8 2.13 0.19 4.78 0.13 0.31 1.26 0.31 4.88 1.15 5 46 49 8.37 58.3 11.2 BS11 J_RUB 0_5 5.4 3.9 5.76 0.37 32.84 0.46 0.68 2.46 0.33 3.39 1.76 9 52 39 8.47 40.0 15.6 BS11 J_RUB 5_10 5.3 3.9 3.2 0.27 10.17 0.25 0.45 1.71 0.23 3.98 1.53 9 50 41 8.38 47.5 11.9 BS11 J_RUB 10_20 5.2 3.8 2.44 0.23 5.44 0.25 0.42 1.84 0.32 3.77 1.26 9 42 49 8.13 46.4 10.6 BS11 J_RUB 20_30 5.1 3.8 2.11 0.2 1.3 0.27 0.52 1.72 0.34 3.1 1.02 7 33 60 7.21 43.0 10.6 BS12 IMP 0_5 5.8 4.1 2.19 0.13 8.27 0.2 0.36 1.56 1.04 1.21 0.05 66 14 20 5.39 22.4 16.8 BS12 IMP 5_10 5.5 4.2 2.03 0.12 6.25 0.12 0.37 1.35 0.41 1.03 0.61 67 11 22 3.33 30.9 16.9 BS12 IMP 10_20 5.3 3.8 1.78 0.1 1.2 0.11 0.31 1.35 0.73 1.51 0.31 69 9 22 4.62 32.7 17.8 BS12 IMP 20_30 5.2 3.9 1.22 0.09 1.2 0.05 0.22 1.56 0.52 2 0.39 61 13 26 4.66 42.9 13.6 BS13 IMP 0_5 5.7 4 2.23 0.13 4.15 0.09 0.42 1.12 0.51 1.18 0.67 66 13 21 3.71 31.8 17.2 BS13 IMP 5_10 5.6 4 2.1 0.12 3.16 0.2 0.45 1.12 0.71 1.48 0.68 67 5 28 4.63 32.0 17.5 BS13 IMP 10_20 5.4 4 2.07 0.12 2.66 0.18 0.44 1.72 0.41 1.78 0.19 65 8 27 5.21 34.2 17.3 BS13 IMP 20_30 5.4 4 1.51 0.09 1.67 0.14 0.41 1.34 1.02 1.78 0.38 65 8 27 4.88 36.5 16.8 BS14 CAS 0_5 5 3.8 1.51 0.11 18.02 0.11 0.25 1.02 0.81 2.19 0.09 61 16 23 4.76 46.0 13.7 BS14 CAS 5_10 5 3.8 1.27 0.1 6.13 0.1 0.24 1.63 0.94 2.07 0.82 57 16 27 5.07 40.8 12.7 BS14 CAS 10_20 5 3.8 0.97 0.09 2.21 0.06 0.23 2.29 0.63 2.12 0.71 54 19 27 6.15 34.5 10.8 BS14 CAS 20_30 4.8 3.8 0.49 0.05 0.19 0.05 0.22 1.56 1.04 2.48 0.41 51 16 33 6.06 40.9 9.8 BS15 CAS 0_5 5.1 3.9 1.78 0.12 17.36 0.11 0.36 2.08 0.45 1.51 0.5 68 13 19 4.92 30.7 14.8 BS15 CAS 5_10 5.1 3.8 1.7 0.11 7.77 0.11 0.34 1.56 0.52 1.51 0.69 61 18 21 4.54 33.3 15.5 BS15 CAS 10_20 5.2 3.9 1.62 0.1 6.76 0.11 0.31 1.56 1.04 1.81 0.7 60 16 24 5.52 32.8 16.2 BS15 CAS 20_30 5.2 3.9 1.38 0.1 4.23 0.08 0.29 1.56 0.41 1.81 0.7 60 16 24 4.85 37.3 13.8 BS16 CHROM 0_5 5.7 4.2 4.66 0.32 35.1 0.48 0.88 2.64 2.41 1.2 0.88 9 66 25 8.31 14.4 14.6 BS16 CHROM 5_10 5.3 3.9 3.64 0.28 17.92 0.28 0.71 2.28 0.57 2.65 1.49 9 59 32 7.37 36.0 13.0 BS16 CHROM 10_20 4.9 3.8 2.72 0.2 6.49 0.27 0.61 2.96 0.22 2.85 1.43 6 57 37 8.4 33.9 13.6 BS16 CHROM 20_30 4.8 3.7 2.27 0.16 3.37 0.12 0.54 1.62 0.54 3.45 1.12 10 52 38 7.7 44.8 14.2

32 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 1 Sapindaceae Xerospermum noronhianum Bl. BS01 XERONORO no-co-do-ph 2 Burseraceae Dacryodes rugosa (Bl.) H.J. Lam BS01 DACRRUGO no-la-do-ct-ph 3 Fabaceae Sindora leiocarpa Backer ex. K. Heyne BS01 SINDLEIO mi-ve-do-ph 4 Myristicaceae Knema cinerea (Poir.) Warb. BS01 KNEMCINE no-la-do-ph 5 Myrtaceae Eugenia ochneocarpa Merr. BS01 EUGEOCHN no-co-do-ph 6 Myristicaceae Knema mandahoran (Miq.) Warb. BS01 KNEMMAND me-co-do-ph 7 Sterculiaceae Scaphium macropodum (Miq.) Beumee BS01 SCAPMACR me-la-do-de-ph 8 Annonaceae Polyalthia lateriflora (Bl.) King. BS01 POLYLATE me-la-do-ph 9 Sapotaceae Palaquium gutta (Hook.f.) Baillon BS01 PALAGUTT no-co-do-ph 10 Myristicaceae Horsfieldia grandis (Bl.) Warb BS01 HORSGRAN pl-la-do-ph 11 Burseraceae Santiria graffithii (Hook.f.) Engl. BS01 SANTGRAF no-ve-do-ph 12 Myrtaceae Eugenia palembanica (Miq.) Merr. BS01 EUGEPALE no-ve-do-ph 13 Theaceae Gordonia sp. BS01 GORDSPP. no-co-do-ph 14 Fabaceae Koompasia malaccensis Maing. ex Benth. BS01 KOOMMALA mi-ve-do-ph 15 Trigoniaceae Trigoniastrum hypoleucum Miq. BS01 TRIGHYPO mi-la-do-ph 16 Ulmaceae Gironniera hirta Ridl. BS01 GIROHIRT no-la-do-ph 17 Dipterocarpaceae Shorea macropera Dyer. BS01 SHORMACR no-ve-do-ph 18 Moraceae Artocarpus anysophyllus Miq. BS01 ARTOANYS no-la-do-ph 19 Euphorbiaceae Drypetes longifolia Pax. & Hoffm. BS01 DRYPLONG no-la-do-ct-ph 20 Fabaceae Fordia johorensis Whitmore BS01 FARDJOHO no-la-do-ct-ph 21 Thymelaeaceae Gonystylus maingayi Hk.f. BS01 GONYMAIN no-la-do-ct-ph 22 Connaraceae Agelaea borneensis (Hook.f.) Merr. BS01 AGELBORN mi-la-do-ct-ph 23 Lecythidaceae Barringtonia scortechinii King. BS01 BARRSCOR no-la-do-ph 24 Rubiaceae Timonius stipulosus (Scheff.) Boerl.. BS01 TIMOSTIP me-la-do-ph 25 Dilleniaceae Tetracera scandens (L.) Merr. BS01 TETRSCAN me-la-do-ph-li 26 Connaraceae Connarus monocarpus L. BS01 CONNMONO no-la-do-ph-li 27 Arecaceae Licuala spinosa Wurmb. BS01 LICUSPIN ma-la-do-ro-pv-ph 28 Burseraceae Dacryodes incurvata (Engler.) H.J. Lam BS01 DACRINCU me-la-do-ph 29 Flacourtiaceae Hydrocarpus polipetala (v. SLoot) Sleumer. BS01 HYDRPOLI no-la-do-ph 30 Sapotaceae Madhuca sandakaensis van Royen BS01 MADHSAND no-la-do-ph 31 Celastraceae Bhesa paniculata Arn. BS01 BHESPANI me-la-do-ph 32 Annonaceae Polyalthia beccari King. BS01 POLYBECC no-la-do-ph 33 Connaraceae Agelaea macrophylla (Zoll.) Leenh. BS01 AGELMACR me-la-do-ph-li 34 Fabaceae Derris sp. BS01 DERRSPP. na-la-do-ph-li 35 Arecaceae Licuala ferruginea Becc. BS01 LICUFERR me-la-do-ro-pv-hc 36 Thymelaeaceae Gonystylus velutinus Airy Shaw BS01 GONYVELU no-la-do-ph 37 Connaraceae Rourea minor (Gaertn.) Leenh. BS01 ROURMINO mi-la-do-ph-li 38 Euphorbiaceae Aporusa subcaudata Merr. BS01 APORSUBC no-la-do-ph 39 Piperaceae Piper sp1. BS01 PIPESPP1 mi-la-do-su-hc-ad-ep 40 Annonaceae Desmos chinensis Lour. BS01 DESMCHIN mi-la-do-ph-li 41 Apocynaceae Willughbeia coriacea Lour. BS01 WILLCORI no-la-do-ph-li 42 Polygalaceae Xanthophyllum affine Korth. BS01 XANTAFFI no-la-do-ph 43 Icacinaceae Gonocaryum gracile Miq. BS01 GONOGRAC me-la-do-ct-ph 44 Annonaceae Goniothalamus macrophyllus (Bl.) Hook.f. & Thoms BS01 GONIMACR me-la-do-ct-ph 45 Dilleniaceae Dillenia borneensis Hogl. BS01 DILLBORN pl-la-do-ph 46 Fagaceae Lithocarpus indutus (Bl.) Rehd. BS01 LITHINDU pl-la-do-ph 47 Sapotaceae Madhuca sericeae (Miq.) H.J. Lam BS01 MADHSERI no-la-do-ph 48 Sterculiaceae Buettneria curtisii Oliv. BS01 BUETCURT mi-la-do-ph-li 49 Sapotaceae Palaquium elasiphyllum (de Vriese) Pierre ex Dubard BS01 PALAELAS me-la-do-ct-ph 50 Arecaceae Calamus javensis Bl. BS01 CALAJAVA me-la-do-ro-pv-ph-li

33 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 51 Arecaceae Daemonorops sp. BS01 DAEMSPP. no-la-do-pv-ph-li 52 Myristicaceae Knema sp2. BS01 GYMNSPP2 me-la-do-ph 53 Connaraceae Agelaea sp1. BS01 AGELSPP1 mi-la-do-ph-li 54 Connaraceae Ellipanthus tomentosus Kurz. BS01 ELLITOME mi-la-do-ph-li 55 Fabaceae Spatholobus sp. BS01 SPATSPP. no-la-do-ph-li 56 Euphorbiaceae Ptychopyxis costata Miq. BS01 PTYCCOST me-la-do-ct-ph 57 Rhizophoraceae Anisophylla disticha (Jack) Baillon BS01 ANISDIST na-la-do-ph 58 Fabaceae Parkia speciosa Hassk. BS01 PARKSPEC no-la-do-ct-ph 59 Sapindaceae Nephelium maingayi Hiern BS01 NEPHMAIN no-la-do-ct-ph 60 Vitaceae Cissus simplex Blanco BS01 CISSSIMP no-la-do-ph-li 61 Connaraceae Connarus semidecandrus Jack. BS01 CONNSEMI no-la-do-ph-li 62 Euphorbiaceae Glochidion superbum Baill. BS01 GLOCSUPE me-la-do-ph 63 Annonaceae Artabotrys sp 1. BS01 ARTASPP1 me-la-do-ph-li 64 Flagellariaceae Flagellaria indica L. BS01 FLAGINDI no-co-do-pv-ph-li 65 Euphorbiaceae Antidesma sp. BS01 APORSPP. no-la-do-ct-ph 66 Meliaceae Dysoxylum macrocarpum Bl. BS01 DYSOMACR me-la-do-ph 67 Myrtaceae Eugenia inophylla Roxb. BS01 EUGEINOP no-la-do-ph 68 Rubiaceae Tarrena fragrans K. et V. BS01 TARRFRAG no-la-do-ph 69 Annonaceae Xylopia malayana Hook.f. & Thoms BS01 XYLOMALA mi-la-do-ct-ph 70 Rosaceae Prunus grissea (C.Muell.) Kalkman BS01 PRUNGRIS no-la-do-ct-ph 71 Polygalaceae Xanthophyllum rufum A.W. Benn. BS01 XANTRUFU no-la-do-ct-ph 72 Connaraceae Rourea mimosoides (Vahl.) Planch. BS01 ROURMIMO na-la-do-ph-li 73 Lauraceae Cryptocarya sumatrana Kosterm. BS01 CRYPSUMA me-la-do-ct-ph 74 Loganiaceae Strychnos ignatii Berg. BS01 STRYIGNA mi-la-do-ph-li 75 Juglandaceae Engelhardtia serrata Bl. BS01 ENGESERR mi-la-do-ct-ph 76 Vitaceae Cissus sp. BS01 CISSSPP. no-la-do-ph-li 77 Sabiaceae Meliosma symplicifolia (Roxb.) Walp.. BS01 MELISYMP pl-la-do-ct-ph 78 Myristicaceae Gymnacranthera contracta Warb. BS01 GYMNCONT me-la-do-ct-ph 79 Burseraceae Santiria laevigata Bl. BS01 SANTLAEV me-la-do-ct-ph 80 Euphorbiaceae Koilodepas longifolium Hook.f. BS01 KOILLONG no-la-do-ct-ph 81 Euphorbiaceae Baccaurea bracteata M.A. BS01 BACCBRAC me-la-do-ph 82 Verbenaceae Teijsmaniodendron coriaceum (C.B. Clarke) Kosterm. BS01 TEIJCORI mi-la-do-ct-ph 83 Fabaceae Phanera sp. BS01 PHANSPP. no-la-do-ph-li 84 Menispermaceae Limacia scandens Lour. BS01 LIMASCAN me-la-do-ph-li 85 Euphorbiaceae Neoscortechinia kingii (Hook.f.) Pax.& K.Hoffm BS01 NEOSKING no-la-do-ph 86 Fagaceae Lithocarpus sp2. BS01 LITHSPP2 no-la-do-ph 87 Burseraceae Dacryodes incurvata (Engler.) H.J. Lam BS01 CANAINCU pl-la-do-ct-ph 88 Melastomataceae Memecylon floribundum Bl. BS01 MEMEFLOR no-la-do-ct-ph 89 Rubiaceae Tricalysia singularis Korth. BS01 TRICSING mi-la-do-ct-ph 90 Myrsinaceae Ardisia sp1. BS01 ARDISPP1 no-la-do-ct-ph 91 Annonaceae Oxymitra grandifolia Merr. BS01 OXYMGRAN pl-la-do-ph-li 92 Burseraceae Canarium littorale Blume BS01 CANALITT mi-co-do-ph 93 Rubiaceae Psychotria sp1. BS01 PSYCSPP1 no-ve-do-hc-ad-ep 94 Euphorbiaceae Baccaurea deflexa M.A. BS01 BACCDEFL me-la-do-ct-ph 95 Icacinaceae Sarcostigma paniculata Pierre BS01 SARCPANI me-la-do-ph-li 96 Anacardiaceae Indet BS01 ANACINDE me-ve-do-ph 97 Aspleniaceae Diplazium sp. BS01 DIPLSPP. no-la-do-fi-hc 98 Orchidaceae Apostasia wallichii R.Br. BS01 APOSWALL mi-la-do-pv-hc 99 Myrsinaceae Labisia acuta Ridl. BS01 LABIACUT no-la-do-su-hc 100 Zingiberaceae Globba sp2. BS01 GLOBSPP2 mi-la-do-su-hc-ad 101 Orchidaceae Calanthe sp. BS01 CALASPP. no-la-do-pv-hc-ad 102 Zingiberaceae Globba paniculata Val. BS01 GLOBPANI no-ve-do-hc-ad

34 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 103 Aspleniaceae Asplenium nidus L. BS01 ASPLNIDU pl-ve-do-ro-fi-hc-ep 104 Burseraceae Santiria laevigata Blume BS02 SANTLAEV me-co-do-ph 105 Clusiaceae Garcinia dioica Blume BS02 GARCDIOI mi-la-do-ph 106 Annonaceae Monocarpia marginalis (Scheff.) J.Sinc. BS02 MONOMARG me-co-do-ph 107 Arecaceae Licuala ferruginea Becc. BS02 LICUFERR ma-la-do-ro-pv-hc 108 Fabaceae Fordia johorensis T.C. Whitm. BS02 FORDJOHO mi-la-do-ct-ph 109 Melastomataceae Memecylon myrsinoides Blume BS02 MEMEMYRS mi-la-do-ct-ph 110 Rhizophoraceae Anisophylla disticha (Jack) Baillon BS02 ANISDIST no-la-do-ct-ph 111 Rhamnaceae Ventilago oblongifolia Blume BS02 VENTOBLO no-la-do-ph-li 112 Clusiaceae Garcinia morella Desr. BS02 GARCMORE mi-la-do-ph 113 Fabaceae Bauhinia kockiana Korth. BS02 BAUHKOCK no-la-do-ph-li 114 Clusiaceae Garcinia scorthechinii King BS02 GARCSCOR mi-la-do-ph 115 Sapotaceae Pouteria sp1 BS02 POUTSP1 me-la-do-ct-ph 116 Lauraceae Cinnamomum iners Reinw. ex Blume BS02 CINNINER me-la-do-ph 117 Burseraceae Santiria sp. BS02 SANTSPP. me-la-do-ph 118 Burseraceae Dacryodes rostrata (Blume) H.J. Lam BS02 DACRROST no-la-do-ct-ph 119 Myrtaceae Syzygium suringarianum (Koord. & Valeton) Amshoff BS02 SYZYSURI me-la-do-ph 120 Sapotaceae Palaquium obovatum (Griff.) Engl. BS02 PALAOBOV me-la-do-ph 121 Meliaceae Walsura sp. BS02 WALSSPP. me-la-do-ph 122 Dipterocarpaceae Parashorea lucida Kurz BS02 PARALUCI me-la-do-ct-ph 123 Polygalaceae Xanthopyllum incertum (Bl.) R. van der Meijden BS02 XANTINCE me-la-do-ct-ph 124 Dipterocarpaceae Shorea pauciflora King BS02 SHORPAUC me-la-do-ph 125 Burseraceae Santiria griffithii Engl. BS02 SANTGRIF no-la-do-ph 126 Linaceae Ixonanthes icosandra Jack. BS02 IXONICOS me-la-do-ph 127 Clusiaceae Calophyllum venulosum Zoll. BS02 CALOVENU me-la-do-ph 128 Connaraceae Rourea mimosoides (Vahl.) Planch. BS02 ROURMIMO na-la-do-ph-li 129 Euphorbiaceae Neoscortechinia kingii (Hook.f.0 Pax & K. Hoffm. BS02 NEOSKING me-la-do-ct-ph 130 Liliaceae Smilax cf. celebica Bl. BS02 SMILCELE no-la-do-ph-li 131 Connaraceae Agelaea borneensis (Hook.f.) Merr. BS02 AGELBORN me-la-do-ph-li 132 Myristicaceae Knema cinerea (Poir.) Warb. BS02 KNEMCINE me-la-do-ct-ph 133 Arecaceae Calamus sp1 BS02 CALASP1 me-la-do-ro-pv-hc-li 134 Euphorbiaceae Pimeleodendron papaveroides J.J. Smith BS02 PIMEPAPA no-la-do-ct-ph 135 Euphorbiaceae Baccaurea sumatrana Muell.Arg. BS02 BACCSUMA me-la-do-ct-ph 136 Dipterocarpaceae Shorea ovalis (Korth.) Blume BS02 SHOROVAL me-la-do-ph 137 Euphorbiaceae Fahrenheitia pendula (Hassk.) Airy Shaw BS02 FAHRPEND pl-la-do-ct-ph 138 Orchidaceae Tropidia cf. graminae Blume BS02 TROPGRAM mi-co-do-ro-pv-hc-li-ad 139 Apocynaceae Alyxia sp. BS02 ALYXSPP. mi-la-do-ph-li 140 Rubiaceae Lecananthus sp. BS02 LECASPP. mi-la-do-hc-li-ad 141 Lauraceae Alseodaphne sp. BS02 ALSESPP. pl-la-do-ct-ph 142 Connaraceae Cnestis platantha Griff. BS02 CNESPLAT mi-pe-do-ph-li 143 Lauraceae Litsea sp. BS02 LITSSPP. pl-la-do-ct-ph 144 Dipterocarpaceae Shorea macroptera Dyer BS02 SHORMACR me-la-do-ct-ph 145 Dipterocarpaceae Shorea parvifolia Dyer BS02 SHORPARV no-la-do-ct-ph 146 Arecaceae Pinanga coronata (Bl. ex Mart.) Blume BS02 PINACORO me-la-do-ro-pv-ph 147 Sapindaceae Xerospermum sp. BS02 XEROSPP. no-la-do-ct-ph 148 Arecaceae Calamus sp2 BS02 CALASP2 me-la-do-ro-pv-hc-li 149 Orchidaceae Tropidia cf. graminae Blume BS02 TROPGRAM mi-co-do-pv-hc-ad 150 Rubiaceae Urophyllum glabrum Wall BS02 UROPGLAB me-la-do-ch 151 Sapotaceae Palaquium gutta (Hook.f.) Baillon BS02 PALAGUTT no-la-do-ph 152 Sapotaceae Pouteria sp2 BS02 POUTSP2 pl-co-do-ct-ph-ad 153 Apocynaceae Willughbeia flavescens Dyer ex Hook.f. BS02 WILLFLAV me-la-do-ph-li 154 Clusiaceae Mesua beccariana (Baill.) Kosterm BS02 MESUBECC me-la-do-ph

35 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 155 Ulmaceae Gironniera subaequalis Planch. BS02 GYROSUBA no-la-do-ct-ph 156 Olacaceae Ochanostachys amentacea Mast. BS02 OCHAAMEN no-la-do-ph 157 Moraceae Artocarpus lanceaefolia Roxb. BS02 ARTOLANC me-la-do-ct-ph 158 Rubiaceae Diplospora singularis Korth. BS02 DIPLSING no-la-do-ct-ph 159 Dilleniaceae Tetracera scandens (L.) Merrill BS02 TETRSCAN no-la-do-ph-li 160 Menispermaceae Limacia scandens Lour BS02 LIMASCAN me-la-do-ph-li 161 Connaraceae Agelaea macrophylla (Zoll.) Leenh. BS02 AGELMACR no-la-do-ph-li 162 Fabaceae Koompassia malaccensis Maing. ex Benth. BS02 KOOMMALA mi-co-do-ph 163 Liliaceae Dracaena angustifolia Roxb. BS02 DRACANGU me-la-do-ro-su-pv-hc-ad 164 Rubiaceae Pavetta montana Reinw. ex Blume BS02 PAVEMONT no-la-do-ct-ph 165 Fagaceae Lithocarpus sp. BS02 LITHSPP. pl-la-do-ph-ad 166 Myrtaceae Syzygium suringarianum (Koord. & Valeton) Amshoff BS02 SYZYSURI no-la-do-ph 167 Polygalaceae Xanthopyllum flavescens Roxb. BS02 XANTFLAV no-la-do-ph 168 Zingiberaceae Alpinia sp. BS02 ALPISPP. me-la-do-su-pv-hc-ad 169 Rosaceae Parinari sp. BS02 PARISPP. mi-la-do-ph 170 Pandanaceae Freycinetia sumatrana Hemsl. BS02 FREYSUMA no-co-do-ro-pv-hc-li-ad 171 Sapindaceae Xerospermum noronhianum Blume BS02 XERONORO me-la-do-ph 172 Icacinaceae Cantleya corniculata (Becc.) Howard BS02 CANTCORN no-la-do-ct-ph 173 Fabaceae Spatholobus ferrugineus (Zoll. & Mor.) Benth. BS02 SPHAFERR me-la-do-ph-li 174 Burseraceae Canarium littorale Blume BS02 CANALITT no-la-do-ph 175 Anacardiaceae Swintonia sp. BS02 SWINSPP. no-co-do-ph 176 Liliaceae Smilax macrocarpa Blume BS02 SMILMACR me-la-do-ch-li-ad-ep 177 Burseraceae Dacryodes costata (A.W. Been.) H.J. Lam BS02 DACRCOST no-la-do-ph 178 Burseraceae Dacryodes incurvata (Engler) H.J. Lam BS02 DACRINCU me-la-do-ph 179 Apocynaceae Willughbeia coriacea Wall BS02 WILLCORI me-la-do-ph-li 180 Fabaceae Dalbergia rostrata Hassk. BS02 DALBROST mi-la-do-ph-li 181 Anacardiaceae Pentaspadon velutinus Hook.f. BS02 PENTVELU no-la-do-ph 182 Euphorbiaceae Coelodepas brevipes Merrill BS02 COELBREV me-la-do-ct-ph 183 Dipterocarpaceae Dipterocarpus lowii Hook.f. BS02 DIPTLOWI me-ve-do-ph 184 Euphorbiaceae Croton argyratus Blume BS02 CROTARGY me-la-do-ct-ph 185 Celastraceae Salacia sp. BS02 SALASPP. me-la-do-ph-li-ep 186 Ulmaceae Gironniera nervosa Planch BS02 GYRONERV me-la-do-ct-ph 187 Euphorbiaceae Aporusa sp. BS02 APORSPP. no-la-do-ph 188 Rutaceae Acronichia laurifolia Blume BS02 ACROLAUR me-la-do-ph 189 Meliaceae Lansium aqueum (Jack) Kosterm BS02 LANSAQUE me-la-do-ct-ph 190 Rubiaceae Gardenia anisophylla Jack ex Roxb. BS02 GARDANIS pl-la-do-ph 191 Celastraceae Salacia macrophylla Blume BS02 SALAMACR me-la-do-ct-ph 192 Lauraceae Cryptocarya sp. BS02 CRYPSPP. me-la-do-ph 193 Araceae Anadendron montanum BS02 ANADMONT me-la-do-su-hc-ad-ep 194 Annonaceae Popowia tomentosa Maing. ex Hook.f. & Thoms. BS02 POPOTOME no-la-do-ph 195 Polypodiaceae Drynaria sparsiosora (Desv.) Moore BS02 DRYNSPAR pl-ve-do-fi-hc-ad-ep 196 Sterculiaceae Scaphium macropodum (Miq.) Beumee BS02 SCAPMACR me-co-do-de-ph 197 Moraceae Ficus sp. BS02 FICUSPP. na-ve-do-hc-ad-ep 198 Myrsinaceae Embelia sp. BS02 EMBESPP. mi-la-do-ph-li 199 Annonaceae Polyalthia sumatrana (Miq.) Kurz BS02 POLYSUMA me-la-do-ph 200 Euphorbiaceae Ptychopyxix kingii Ridley BS02 PTYCKING me-la-do-ph 201 Loganiaceae Fragaea recemosa Jack ex Wall. BS02 FRAGRECE me-la-do-ph 202 Gnetaceae Gnetum latifolium Blume BS02 GNETLATI me-la-do-ph-li 203 Anacardiaceae Pentaspadon velutinus Hook.f. BS02 PENTVELU mi-co-do-ph 204 Meliaceae Aglaia tomentosa Teijsm. & Binn. BS02 AGLATOME no-la-do-ph 205 Burseraceae Santiria oblongifolia Blume BS02 SANTOBLO me-la-do-ph

36 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 206 Rubiaceae Lansianthus scabridus King & Gamble BS02 LANSSCAB me-la-do-ct-ph 207 Simaroubaceae Eurycoma longifolia Jack BS02 EURYLONG mi-la-do-ph 208 Dilleniaceae Dillenia ovata Wall. ex Hook.f. & Thoms. BS03 DILLOVAT pl-la-do-ph-ad 209 Gleicheniaceae Dicranopteris linearis (Burms.f.) Underw. BS03 DICRLINE na-la-do-fi-hc-li-ad 210 Rubiaceae Uncaria glabrata (Blume) DC. BS03 UNCAGLAB me-la-do-ph-li 211 Rubiaceae Adina minutiflora Valeton BS03 ADINMINU no-la-do-ct-ph 212 Dipterocarpaceae Shorea acuminata Dyer BS03 SHORACUM no-la-do-ct-ph 213 Myrsinaceae Embelia dasythyrsa Miq. BS03 EMBEDASY mi-la-do-ph-li 214 Apocynaceae Willughbeia coriacea Wall. BS03 WILLCORI no-la-do-ph-li 215 Dipterocarpaceae Anisoptera costata Korth BS03 ANISCOST pl-la-do-ct-ph 216 Fabaceae Cordia johorensis T.C. Whitm. BS03 CORDJOHO no-la-do-ct-ph 217 Rhizophoraceae Gynotroches axillaris Blume BS03 GYNOAXIL no-la-do-ct-ph 218 Sapotaceae Madhuca cf. sericea (Miq.) Lam. BS03 MADHSERI me-la-do-ct-ph 219 Myristicaceae Knema latericia Elmer BS03 KNEMLATE me-la-do-ph 220 Dipterocarpaceae Shorea macroptera Dyer BS03 SHORMACR me-la-do-ph 221 Burseraceae Canarium littorale Blume BS03 CANALITT me-la-do-ct-ph 222 Orchidaceae Dendrobium secundum (Bl.) Lindl. BS03 DENDSECU mi-ve-do-su-pv-hc-ad-ep 223 Polygalaceae Xanthophyllum rufum A.W.Benn. BS03 XANTRUFU me-la-do-ph 224 Zingiberaceae Hornstedtia sp. BS03 HORNSPP. me-la-do-su-hc-ad 225 Melastomataceae Pternandra rostrata (Cogn.) M.P. Nayar BS03 PTERROST me-la-do-ct-ph 226 Clusiaceae Calophyllum saigonense Pierre BS03 CALOSAIG no-la-do-ct-ph 227 Annonaceae Melodorum kentii Hook.f. & Thoms. BS03 MELOKENT mi-la-do-ph-li 228 Euphorbiaceae Glochidion arborescens Blume BS03 GLOCARBO me-la-do-ph 229 Annonaceae Cyathocalyx bancanus Boerl. BS03 CYATBANC pl-la-do-ct-ph 230 Liliaceae Smilax sp. BS03 SMILSPP. no-pe-do-ph-li 231 Fabaceae Dalbergia rostrata Hassk. BS03 DALBROST mi-la-do-ph-li 232 Fabaceae Spatholobus ferrugineus (Zoll. & Mor.) benth. BS03 SPHAFERR me-la-do-ph-li 233 Fabaceae Sindora velutina Baker BS03 SINDVELU me-la-do-ph 234 Euphorbiaceae Baccaurea sumatrana Muell. Arg. BS03 BACCSUMA no-la-do-ct-ph 235 Dipterocarpaceae Shorea pauciflora King BS03 SHORPAUC no-la-do-ct-ph 236 Menispermaceae Limacia scandens Lour. BS03 LIMASCAN mi-la-do-ph-li 237 Arecaceae Licuala ferruginea Becc. BS03 LICUFERR mg-co-do-ro-pv-hc 238 Annonaceae Monocarpia marginalis (Scheff.) J. Sincl. BS03 MONOMARG me-pe-do-ct-ph 239 Rubiaceae Gardenia anisophylla Jack ex Roxb. BS03 GARDANIS me-la-do-ph 240 Asclepiadaceae Telosma accesdens (Blume) Backer BS03 TELOACCE no-la-do-ph-li 241 Melastomataceae Dissochaeta gracilis Blume BS03 DISSGRAC no-pe-do-ph-li-ad 242 Fabaceae Phanera kockiana Benth BS03 PHANKOCK mi-la-do-ph-li-ep 243 Anacardiaceae Mangifera magnifica K.M. Kochummen BS03 MANGMAGN me-co-do-ph 244 Rhamnaceae Ventilago oblongifolia Blume BS03 VENTOBLO me-la-do-ph-li 245 Moraceae Streblus sp. BS03 STRESPP. me-co-do-ph-li 246 Burseraceae Dacryodes costata (A.W.Been.) H.J. Lam BS03 DACRCOST no-la-do-ph 247 Arecaceae Calamus sp. BS03 CALASPP. me-la-do-ro-pv-ch-li 248 Polypodiaceae Drynaria sparsiosora (Desv.) Moore BS03 DRYNSPAR pl-ve-do-fi-hc-ad-ep 249 Fabaceae Dialium cf. laurinum Baker BS03 DIALLAUR me-la-do-ct-ph 250 Cyperaceae Hypolytrum nemorum (Vahl) Spreng. BS03 HYPONEMO me-co-do-ro-pv-hc 251 Vitaceae Cissus repens Lam. BS03 CISSREPE no-la-do-ph-li 252 Celastraceae Bhesa paniculata Arn. BS03 BHESPANI me-la-do-ct-ph 253 Meliaceae Aglaia sp. BS03 AGLASPP. me-la-do-ph 254 Connaraceae Agelaea macrophylla (Zoll.) Leenh. BS03 AGELMACR me-pe-do-ph-li 255 Burseraceae Santiria sp. BS03 SANTSPP. pl-la-do-ph 256 Fabaceae Indet (empty) BS03 INDET*** mi-la-do-ph-li 257 Olacaceae Ochanostachys amentacea Mast. BS03 OCHAAMEN me-la-do-ct-ph

37 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 258 Sapotaceae Palaquium leiocarpum (Hook.f.) Baillon BS04 PALALEIO no-co-do-ph 259 Dipterocarpaceae Shorea macroptera Dyer. BS04 SHORMACR no-co-do-ph 260 Burseraceae Santiria laevigata Bl. BS04 SANTLAEV no-co-do-ph 261 Fabaceae Crudia teysmanii de Wit. BS04 CRUDTEYS no-la-do-ph 262 Polygalaceae Xanthophyllum rufum A.W. Benn. BS04 XANTRUFU mi-co-do-ph 263 Rhizophoraceae Carallia brachiata (L.) Merrill BS04 CARABRAC mi-co-do-ph 264 Dipterocarpaceae Shorea parvifolia Dyer. BS04 SHORPARV mi-ve-do-ph 265 Annonaceae Cyathocalyx bancana Boerl. BS04 CYATBANC pl-co-do-ct-ph 266 Euphorbiaceae Baccaurea deflexa M.A. BS04 BACCDEFL no-co-do-ph 267 Dipterocarpaceae Shorea lepidota Korth.. BS04 SHORLEPI me-co-do-ph 268 Myrtaceae Syzygium sp1. BS04 SYZYSPP1 no-co-do-ph 269 Moraceae Artocarpus elasticus Reinw. ex Bl. BS04 ARTOELAS ma-co-do-ct-ph 270 Elaeocarpaceae Elaeocarpus macrophyllus Bl. BS04 ELAEGLAB no-co-do-ph-ad 271 Myristicaceae Horsfieldia grandis (Bl.) Warb. BS04 HORSGRAN pl-la-do-ph-ad 272 Olaceae Scorodocarpus borneensis (Baill.) Becc. BS04 SCORBORN me-la-do-ph 273 Dilleniaceae Dillenia obovata (Bl.) Hogl. BS04 DILLOBOV me-la-do-ph-ad 274 Connaraceae Agalaea borneensis (Hook.f.) Merr. BS04 AGALBORN mi-la-do-ph-li 275 Burseraceae Canarium denticulatum Bl. BS04 CANADENT no-la-do-ph 276 Cluciaceae Mesua ferruginea (Pierre.) Kosterm. BS04 MESUFERR no-la-do-ct-ph 277 Celastraceae Kokoona ochracea (Elmer.) Merr. BS04 KOKOOCHR no-la-do-ct-ph 278 Lecythidaceae Barringtonia scortechinii King. BS04 BARRSCOR no-la-do-ph 279 Theaceae Ternstroemia bancana Miq. BS04 TERNBANC mi-la-do-ct-ph 280 Annonaceae Xylopia malayana Hook.f. & Thoms. BS04 XYLOMALA mi-la-do-ph 281 Rubiaceae Gardenia anisophylla Jack ex Roxb BS04 GARDANIS me-la-do-ct-ph 282 Fabaceae Fordia johorensis Whitmore BS04 FORDJOHO no-la-do-ct-ph 283 Myristicaceae Knema cinerea (Poir.) Warb. BS04 KNEMCINE no-co-do-ph 284 Apocynaceae Willughbeia edulis Roxb. BS04 WILLEDUL no-la-do-ph-li 285 Arecaceae Calamus javensis Bl. BS04 CALAJAVE no-la-do-pv-ph-li 286 Arecaceae Calamus sp1. BS04 CALASPP1 me-la-do-pv-ph-li 287 Loganiaceae Fagraea racemosa Jack ex Wall. BS04 FAGRRACE me-la-do-ct-ph 288 Annonaceae Polyalthia lateriflora (Bl.) King BS04 POLYLATE no-la-do-ph 289 Ebenaceae Diospyros sp1. BS04 DIOSSPP1 me-la-do-ph 290 Rubiaceae Timonius stipulosus (Scheff.) Boerl. BS04 TIMOSTIP me-la-do-ct-ph 291 Euphorbiaceae Aporusa subcaudata Merr. BS04 APORSUBC no-la-do-ph 292 Thymelaeaceae Gonystylus maingayi Hook.f. BS04 GONYMAIN no-la-do-ct-ph 293 Sapotaceae Pouteria malaccensis (Clarke) Baehni BS04 POUTMALA me-la-do-ct-ph 294 Linaceae Ixonanthes petiolaris Bl. BS04 IXONPETI me-la-do-ph 295 Euphorbiaceae Trigonopleura malayana Hook.f. BS04 TRIGMALA me-la-do-ph 296 Myrtaceae Syzygium splendens (Bl.) Merr. BS04 SYZYSPLE mi-la-do-ct-ph-ad 297 Fagaceae Lithocarpus sp2. BS04 LITHSPP2 me-la-do-ph 298 Polygalaceae Xanthophyllum discolor Chod. BS04 XANTDISC me-la-do-ct-ph 299 Ulmaceae Gironniera subaequalis Planch. BS04 GIROSUBA me-la-do-ct-ph 300 Ulmaceae Gironniera nervosa Planch. BS04 GIRONERV no-la-do-ct-ph 301 Elaeocarpaceae Elaeocarpus stipularis Blume BS04 ELAESTIP me-la-do-ct-ph 302 Meliaceae Dysoxylum excelsum Bl. BS04 DYSOEXCE no-la-do-ph 303 Sterculiaceae Scaphium macropodum (Miq.) Beumee. BS04 SCAPMACR pl-la-do-ph 304 Sapotaceae Palaquium dasyphyllum (de Vriese) Pierre ex Dubard. BS04 PALADASY no-la-do-ph 305 Connaraceae Agelaea macrophylla (Zoll.) Leenh. BS04 AGELMACR me-la-do-ph-li 306 Dipterocarpaceae Parashorea malaanonan (Blco.) Merr. BS04 PARAMALA me-la-do-ct-ph 307 Verbenaceae Teijsmanniodendron coriaceum (C.B. Clarke) Kosterm. BS04 TEIJCORI no-la-do-ct-ph 308 Annonaceae Goniothalamus macrophyllus (Bl.) Hook.f. & Thoms. BS04 GONIMACR me-la-do-ct-ph 309 Rhizophoraceae Gynotroches axillaris Blume BS04 GYNOAXIL no-la-do-ct-ph

38 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 310 Euphorbiaceae Glochidion sp. BS04 GLOCSPP. me-la-do-ph 311 Annonaceae Melodorum kentii (Bl.) Miq. BS04 MELOKENT mi-la-do-ph-li 312 Lauraceae Actinodaphne glomerata (Bl.) Nees. BS04 ACTIGLOM me-la-do-ct-ph 313 Burseraceae Dacryodes rugosa (Bl.) H.J. Lam BS04 DACRRUGO no-la-do-ct-ph 314 Myrtaceae Syzygium splendens (Bl.) Merr. BS04 SYZYSPLE no-la-do-ph 315 Euphorbiaceae Galearia filiformis (Bl.) Pax BS04 GALEFILI me-la-do-ct-ph 316 Connaraceae Connarus sp. BS04 CONNSPP. mi-la-do-ph-li 317 Dilleniaceae Dillenia obovata (Bl.) Hogl. BS04 DILLOBLO pl-la-do-ph 318 Dipterocarpaceae Shorea multiflora (Burck.) Sym. BS04 SHORMULT no-la-do-ct-ph 319 Meliaceae Aglaia dookoo Griff. BS04 AGLADOOK pl-la-do-ct-ph 320 Annonaceae Uvaria hirsuta Jack. BS04 UVARHIRS no-la-do-ph-li-ad 321 Apocynaceae Hunteria zeylanica (Retz.) Gardn. BS04 HUNTZEYL mi-la-do-ph-li 322 Fabaceae Phanera sp. BS04 PHANSPP. no-la-do-ph-li 323 Euphorbiaceae Aporusa grandistipula Merr. BS04 APORGRAN me-la-do-ct-ph 324 Myristicaceae Horsfieldia subglobosa (Miq.) Warb. BS04 HORSSUBG me-la-do-ph 325 Arecaceae Calamus perakensis Becc. BS04 CALAPERA me-la-do-ro-pv-hc 326 Arecaceae Calamus sp3. BS04 CALASPP3 me-la-do-ro-pv-ph-li 327 Lauraceae Litsea confusa K.et V. BS04 LITSCONF me-la-do-ct-ph 328 Ebenaceae Diospyros rigida Hiern. BS04 DIOSRIGI me-la-do-ph 329 Melastomataceae Pternandra gaelata (Cogn.) Ridl. BS04 PTERGAEL me-la-do-ph 330 Arecaceae Licuala spinosa Wurmb. BS04 LICUSPIN ma-la-do-ro-pv-hc 331 Rhamnaceae Ventilago dichotoma (Blanco) Merr. BS04 VENTDICH mi-la-do-ph-li 332 Myrsinaceae Ardisia sp1. BS04 ARDISPP1 me-la-do-ct-ph 333 Burseraceae Santiria apiculata Benn. BS04 SANTAPIC no-la-do-ct-ph 334 Rhizophoraceae Anisophylla disticha (Jack.) Baill. BS04 ANISDIST na-la-do-ct-ph 335 Dilleniaceae Tetracera scandens (L.) Merr. BS04 TETRSCAN no-la-do-ph-li 336 Fabaceae Actinodaphne procera Nees. BS04 ACTIPROC me-la-do-ct-ph 337 Rubiaceae Lasianthus scabridus King & Gamble BS04 LASISCAB me-la-do-ct-ph 338 Polygalaceae Xanthophyllum incertum (Bl.) Meijden. BS04 XANTINCE no-la-do-ct-ph 339 Euphorbiaceae Aporusa lucida (Miq.) Airy Shaw. BS04 APORLUCI no-la-do-ct-ph 340 Aquifoliaceae Ilex cymosa Bl. BS04 ILEXCYMO no-la-do-ct-ph 341 Fagaceae Lithocarpus elegans (Bl.) Hattus. ex Soepadmo BS04 LITHELEG me-la-do-ct-ph 342 Rubiaceae Pavetta sylvatica Bl. BS04 PAVESYLV me-la-do-ct-ph 343 Icacinaceae Sarcostigma paniculata Pierre BS04 SARCPANI me-la-do-ph-li 344 Fabaceae Spatholobus ferrugineus (Zoll.) Bth. BS04 SPATFERR me-la-do-ph-li 345 Fabaceae Sindora wallichii Graham ex Benth. BS04 SINDWALL no-la-do-ph 346 Sapindaceae Nephelium uncinatum Radlk ex P.W. Leenhouts BS04 NEPHUNCI no-la-do-ct-ph 347 Burseraceae Santiria oblongifolia Bl. BS04 SANTOBLO no-la-do-ct-ph-ad 348 Dipterocarpaceae Hopea mengarawan Miq. BS04 HOPEMENG mi-la-do-ph-ad 349 Dipterocarpaceae Shorea ovalis (Korth.) Bl. BS04 SHOROVAL me-la-do-ph 350 Rubiaceae Gaertnera vaginans (DC.) Merr. BS04 GAERVAGI pl-la-do-ph 351 Fabaceae Spatholobus maingayi Prain BS04 SPATMAIN me-la-do-ph-li 352 Annonaceae Polyalthia lateriflora (Bl.) King BS04 POLYLATE me-la-do-ph 353 Piperaceae Piper ungaramense DC. BS04 PIPEUNGA no-la-do-su-hc-ad-ep 354 Dipterocarpaceae Shorea acuminata Dyer. BS04 SHORACUM mi-la-do-ph 355 Euphorbiaceae Pimeleodendron papaveroides J.J.Smith. BS04 PIMEPAPA me-la-do-ph 356 Arecaceae Pinanga sp. BS04 PINSSPP. pl-la-do-ro-pv-ph 357 Myristicaceae Knema lunduensis (Sinclair) de Wilde BS04 KNEMLUND me-la-do-ph 358 Burseraceae Dacryodes rostrata (Blume) H.J. Lam BS04 DACRROST me-la-do-ph 359 Flagellariaceae Hanguana malayana (Jack.) Merr. BS04 HANGMALA no-la-do-ro-pv-hc-ad 360 Cluciaceae Garcinia celebica L. BS04 GARCCELE no-la-do-ph 361 Rubiaceae Urophyllum arboreum Korth. BS04 UROPARBO no-la-do-ct-ph

39 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 362 Liliaceae Pleomele elliptica (Thunb.) N.E. Br. BS04 PLEOELLI no-la-do-ro-pv-hc-ad 363 Myrsinaceae Labisia acuta Ridl. BS04 LABIACUT mi-la-do-hc 364 Zingiberaceae Globba paniculata L. BS04 GLOBPANI no-ve-do-pv-hc-ad 365 Pandanaceae Pandanus sp1. BS04 PANDSPP1 me-co-do-ro-pv-hc-ad 366 Adiantaceae Syngramma walichii Hook. BS04 SYNGWALL no-la-do-fi-hc-ad 367 Thelipteridaceae Pteumatopteris callosa (Bl.) Nakai BS04 PTEUCALL na-la-do-ro-fi-hc-ad 368 Myrsinaceae Labisia pumila (Bl.) F. Vill BS04 LABIPUMI no-la-do-su-hc-ad 369 Fabaceae Koompassia malaccensis Maing. ex Benth BS05 KOOMMALA mi-co-do-ph 370 Dipterocarpaceae Shorea macroptera Dyer BS05 SHORMACR no-co-do-ph 371 Clusiaceae Calophyllum molle King BS05 CALOMOLL me-la-do-ph 372 Menispermaceae Fibraurea cf. chloroleuca Miers BS05 FIBRCHLO no-la-do-ph-li 373 Meliaceae Aglaia ganggo Miq. BS05 AGLAGANG pl-la-do-ph 374 Clusiaceae Garcinia dioica Blume BS05 GARCDIOI mi-la-do-ph 375 Ulmaceae Gironniera nervosa Planch BS05 GIRONERV me-la-do-ct-ph 376 Lauraceae Actinodaphne sp. BS05 ACTISPP. no-la-do-ct-ph 377 Euphorbiaceae Aporusa subcaudata Merr. BS05 APORSUBC no-la-do-ct-ph 378 Euphorbiaceae Phyllanthus sp. BS05 PHYLSPP. na-la-do-ct-ph 379 Dipterocarpaceae Shorea acuminata Dyer BS05 SHORACUM no-la-do-ct-ph 380 Lauraceae Litsea sp. BS05 LITSSPP. me-la-do-ct-ph 381 Burseraceae Santiria oblongifolia Blume BS05 SANTOBLO me-la-do-ph 382 Fabaceae Milletia sericea (Vent.) Wight & Arn. BS05 MILLSERI no-la-do-ph-li 383 Annonaceae Xylopia malayana Hook.f. & Thoms. BS05 XYLOMALA mi-la-do-ct-ph 384 Fabaceae Spatholobus ferrugineus (Zoll. & Mor.) Benth. BS05 SPHAFERR me-la-do-ph-li 385 Zingiberaceae Hornstedtia sp. BS05 HORNSPP. pl-la-do-su-hc-ad 386 Myrsinaceae Labisia acuta Ridley BS05 LABIACUT no-la-do-hc-ad 387 Orchidaceae Tropidia sp. BS05 TROPSPP. mi-la-do-pv-hc 388 Flagellariaceae Hanguana malayana (Jack) Merr BS05 HANGMALA me-co-do-ro-su-hc-ad 389 Arecaceae Calamus sp. BS05 CALASPP. me-la-do-ro-pv-ch-li 390 Icacinaceae Gonocaryum littorale (Bl.) Sleum BS05 GONOLITT me-la-do-ch 391 Myristicaceae Knema intermedia (Bl.) Warb. BS05 KNEMINTE me-la-do-ct-ph 392 Sterculiaceae Scaphium macropodum (Miq.) Beumee ex. K. Heyne BS05 SCAPMACR pl-la-do-ph 393 Fabaceae Phanera kockiana Benth. BS05 PHANKOCK no-pe-do-ph-li 394 Myrtaceae Syzygium fastigiatum (Blume) Merrill & Perry BS05 SYZYFAST no-la-do-ph 395 Myrsinaceae Ardisia sp. BS05 ARDISPP. me-la-do-ct-ph 396 Lauraceae Litsea sp. BS05 LITSSPP. pl-la-do-ph 397 Sapotaceae Palaquium obovatum (Griff.) Engl. BS05 PALAOBOV me-la-do-ph 398 Fagaceae Lithocarpus sp. BS05 FAGASPP. me-la-do-ct-ph 399 Euphorbiaceae Neoscortechinia kingii (Hook.f.) Pax & K. Hoffm. BS05 NEOSKING no-la-do-ph 400 Connaraceae Agelaea borneensis (Hook.f.) Merr. BS05 AGELBORN mi-la-do-ph-li 401 Euphorbiaceae Baccaurea motleyana Muell.Arg. BS05 BACCMOTL me-la-do-ph 402 Dilleniaceae Tetracera cf. scandens (L.) Merrill BS05 TETRSCAN me-la-do-ph-li 403 Moraceae Streblus sp. BS05 STRESPP. mi-la-do-ph-li 404 Myristicaceae Knema latericia Elmer BS05 KNEMLATE me-la-do-ct-ph 405 Dipterocarpaceae Shorea ovalis (Korth.) Blume BS05 SHOROVAL me-la-do-ct-ph 406 Euphorbiaceae Aporusa sp. BS05 APORSPP. me-la-do-ph 407 Annonaceae Uvaria purpurea Blume BS05 UVARPURP pl-la-do-ph-li 408 Clusiaceae Garcinia scortechinii King BS05 GARCSCOR mi-la-do-ct-ph 409 Burseraceae Santiria apiculata A.W. Benn. BS05 SANTAPIC no-la-do-ph 410 Clusiaceae Mesua beccariana (Baill.) Kosterm. BS05 MESUBECC no-la-do-ph 411 Celastraceae Salacia macrophylla Blume BS05 SALAMACR mi-la-do-ct-ph 412 Burseraceae Santiria oblongifolia Blume BS05 SANTOBLO no-la-do-ph 413 Clusiaceae Calophyllum pulcherrimum Wall. BS05 CALOPULC mi-co-do-ct-ph

40 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 414 Olacaceae Ochanostachys amentacea Mast. BS05 OCHAAMEN no-la-do-ph 415 Sapotaceae Palaquium gutta (Hook.f.) Baillon BS05 PALAGUTT no-la-do-ph-ad 416 Loganiaceae Strychnos sp. BS05 STRYSPP. no-la-do-ph-li 417 Sterculiaceae Leptonichia heteroclita Kurz BS05 LEPTHETE no-la-do-ct-ph 418 Rubiaceae Pavetta montana Reinw. ex Blume BS05 PAVEMONT no-la-do-ch 419 Sapotaceae Planchonella cf. duclitan (Blanco) Bakh.f. BS05 PLANDUCL no-la-do-ct-ph 420 Anacardiaceae Mangifera magnifica K.M. Kochummen BS05 MANGMAGN me-co-do-ph 421 Arecaceae Daemonorops sp. BS05 DAEMSPP. me-la-do-ro-pv-ph-li-ad 422 Arecaceae Licuala ferruginea Becc. BS05 LICUFERR pl-la-do-ro-pv-hc 423 Rubiaceae Gardenia anisophylla Jack ex Roxb. BS05 GARDANIS me-la-do-ph 424 Polygalaceae Xanthophyllum incertum (Bl.) R. van der Meijden BS05 XANTINCE me-la-do-ct-ph 425 Euphorbiaceae Drypetes longifolia Pax & Hoffm. BS05 DRYPLONG me-la-do-ct-ph 426 Arecaceae Areca sp. BS05 ARECSPP. me-co-do-ro-pv-hc-ad 427 Fabaceae Indet 1 (empty) BS05 INDE(EMP mi-pe-do-ph-li 428 Apocynaceae Willughbeia coriacea Wall. BS05 WILLCORI no-la-do-ph-li 429 Polygalaceae Xanthophyllum sp. BS05 XANTSPP. no-la-do-ct-ph 430 Burseraceae Dacryodes costata (A.W. Been.) H.J. Lam BS05 DACRCOST me-la-do-ct-ph 431 Annonaceae Cyathoxalyx bancanus Boerl. BS05 CYATBANC pl-la-do-ph 432 Clusiaceae Calophyllum soulattri Burm.f. BS05 CALOSOUL me-la-do-ph 433 Annonaceae Goniothalamus macrophyllus (Bl.) Hook.f. & Thoms. BS05 GONIMACR me-la-do-ct-ph 434 Flacourtiaceae Hydnocarpus kunstleri (King) Warb. BS05 HYDNKUNS no-la-do-ct-ph 435 Euphorbiaceae Coelodepas brevives Merrill BS05 COELBREV no-la-do-ph 436 Rubiaceae Gardenia anisophylla Jack ex Roxb. BS05 GARDANIS pl-la-do-ct-ph 437 Rosaceae Prunus arborea (Blume) Kalkman BS05 PRUNARBO me-la-do-ph 438 Symplocaceae Symplocos sp. BS05 SYMPSPP. no-la-do-ph 439 Liliaceae Dracaena angustifolia Roxb. BS05 DRACANGU me-co-do-ro-su-pv-hc-ad 440 Dilleniaceae Dillenia ovata Wall. ex Hook.f. & Thoms. BS05 DRACOVAT pl-la-do-ct-ph 441 Euphorbiaceae Antidesma stipulare Blume BS05 ANTISTIP no-la-do-ct-ph 442 Annonaceae Indet 2 (empty) BS05 INDE(EMP mi-la-do-ph-li 443 Elaeocarpaceae Elaeocarpus petiolatus (Jack) Wall. BS05 ELAEPETI me-la-do-ph 444 Lauraceae Litsea firma (Bl.) Hook.f. BS05 LITSFIRM me-la-do-ct-ph 445 Gnetaceae Gnetum cuspidatum Blume BS05 GNETCUSP no-la-do-ph-li 446 Fabaceae Parkia sumatrana Miq. BS05 PARKSUMA na-la-do-ct-ph 447 Icacinaceae Indet 3 (empty) BS05 INDE(EMP me-la-do-ph-li 448 Rhizophoraceae Anisophylla disticha (Jack) Baillon BS05 ANISDIST na-la-do-ct-ph 449 Lauraceae Litsea accendens (Bl.) Boerl. BS05 LITSACCE me-la-do-ct-ph 450 Fabaceae Sindora leiocarpa Backer ex K. Heyne BS05 SINDLEIO no-la-do-ph 451 Clusiaceae Calophyllum sp. BS05 CALOSPP. me-la-do-ct-ph 452 Clusiaceae Garcinia parvifolia (Miq.) Miq. BS05 GARCPARV me-la-do-ct-ph 453 Dipterocarpaceae Parashorea lucida Kurz BS05 PARALUCI me-la-do-ph 454 Myrtaceae Syzygium densiflorum Brongn. & Gris BS05 SYZYDENS me-la-do-ph 455 Myrtaceae Syzygium cf. acuminatissimum DC. BS05 SYZYACUM mi-la-do-ct-ph 456 Sapotaceae Pouteria sp. BS05 POUTSPP. me-la-do-ct-ph 457 Euphorbiaceae Pimeleodendron papaveroides J.J. Smith BS05 PIMEPAPA me-la-do-ct-ph 458 Lauraceae Dehaasia firma Blume BS05 DEHAFIRM me-co-do-ct-ph 459 Lauraceae Phoebe elliptica Blume BS05 PHOEELLI pl-la-do-ph 460 Rosaceae Parinari sp. BS05 PARISPP. mi-la-do-ct-ph 461 Fabaceae Fordia johorensis T.C. Whitm. BS05 FORDJOHO mi-la-do-ct-ph 462 Rubiaceae Lasianthus scabridus King & Gamble BS05 LASISCAB me-la-do-ch 463 Anacardiaceae Melanochyla sp. BS05 MELASPP. pl-la-do-ct-ph 464 Fabaceae Dialium sp. BS05 DIALSPP. mi-co-do-ct-ph 465 Vittaria Group Vittaria sp. BS05 VITTSPP. mi-ve-do-fi-hc-ad-ep

41 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 466 Arecaceae Licuala ferruginea Becc. BS05 LICUFERR ma-la-do-ro-pv-ph 467 Burseraceae Santiria tomentosa Blume BS05 SANTTOMR me-la-do-ph 468 Myrtaceae Syzygium sp. BS05 SYZYSPP. me-la-do-ct-ph-ad 469 Fabaceae Archidendron jiringa (Jack) I. Nielsen BS05 ARCHJIRI mi-la-do-ct-ph 470 Sterculiaceae Sterculia subpeltata Blume BS05 STERSUBP me-la-do-ct-ph 471 Myristicaceae Horsfieldia grandis (Bl.) Warb. BS05 HORSGRAN pl-la-do-ph 472 Thymelaeaceae Trigoniastrum hypoleucum Miq. BS05 TRIGHYPO no-la-do-ph 473 Euphorbiaceae Diospyros sp. BS05 DIOSSPP. me-la-do-ph 474 Araceae Santiria oblongifolia Blume BS05 SANTOBLO pl-la-do-ph 475 Thymelaeaceae Gonystylus bancanus (Miq.) Kurz BS05 GONYBANC me-la-do-ct-ph 476 Euphorbiaceae Ptychopyxis kingii Ridley BS05 PTYCKING me-la-do-ph 477 Melastomataceae Pternandra arzurea (Bl.) Burk. BS05 PTERARZU mi-la-do-ph 478 Euphorbiaceae Fahrenheitia pendula (Hassk.) Airy Shaw BS05 FAHRPEND me-la-do-ct-ph 479 Araceae Epipremnum cf. grandifolium Engl. BS05 EPIPGRAN ma-la-do-su-hc-li-ad-ep 480 Myristicaceae Gymnacranthera forbesii (King) Warb. BS05 GYMNFORB no-la-do-ph 481 Melastomataceae Memecylon myrsinoides Blume BS05 MEMEMYRS mi-la-do-ph 482 Liliaceae Dracaena elliptica Thunb. BS05 DRACELLI mi-la-do-pv-ch 483 Melastomataceae Memecylon edule Roxb. BS05 MEMEEDUL no-la-do-ph 484 Verbenaceae Teijsmanniodendron coriaceum (C.B.Clarke) Kosterm. BS05 TEIJCORI mi-la-do-ct-ph 485 Sterculiaceae Leptonichia heteroclita Kurz BS05 LEPTHETE no-pe-do-ct-ph 486 Ulmaceae Trema cannabina Lour. BS06 TREMCANN mi-la-do-ct-ph 487 Euphorbiaceae Macaranga javanica M.A. BS06 MACAJAVA me-la-do-ct-ph 488 Euphorbiaceae Macaranga caladifolia M.A. BS06 MACAGIGA no-la-do-ct-ph 489 Euphorbiaceae Macaranga gigantea M.A. BS06 MACAGIGA ma-pe-do-ct-ph 490 Melastomataceae Mikania cordata (Burm.f.) B.L. Robinson BS06 MIKACORD no-la-do-hc-li 491 Asteraceae Eupatorium odoratum L.F. BS06 EUPAODOR no-pe-do-ch 492 Cyperaceae Scleria purpurascens Steud. BS06 SCLEPURP me-la-do-pv-hc 493 Dennstaedtiaceae Pteridium aquilinum Kuhn. BS06 PTERAQUI le-la-do-fi-hc-ad 494 Asteraceae Blumea lacera (Burm.f.) DC. BS06 BLUMLACE mi-ve-do-ch 495 Melastomataceae Melastoma affine D.Don. BS06 MELAAFFI mi-la-do-ch 496 Davallia Group Nephrolepis biserrata Schott. BS06 NEPHBISE mi-la-do-fi-hc-ad 497 Rubiaceae Uncaria sclerophylla (Hunter) Roxb. BS06 UNCASCLE mi-la-do-ph-li 498 Rubiaceae Uncaria glabrata DC. BS06 UNCAGLAB no-la-do-ph-li 499 Poaceae Centotheca lappacea (L.) Desvaux BS06 CENTLAPP mi-la-do-ro-pv-hc 500 Asclepiadaceae Gynanchum ovalifolium Wight. BS06 GYNAOVAL no-ve-do-ph-li 501 Annonaceae Artabotrys sp1. BS06 ARTASPP1 mi-la-do-ph-li 502 Meliaceae Sandoricum koetjape (Burm.f.) Merr. BS06 SANDKOET no-la-do-ph 503 Poaceae Axonophus compressus (Swartz) Beauv. BS06 AXONCOMP mi-ve-do-pv-hc-ad 504 Poaceae Imperata cylindrica (nees.) C.E. Hubb. BS06 IMPECYLI me-ve-do-pv-hc-ad 505 Solanaceae Solanum torvum Swartz. BS06 SOLATORV me-la-do-ch 506 Lecythidaceae Barringtonia scortechinii King. BS06 BARRSCOR me-la-do-ct-ph 507 Euphorbiaceae Bridellia monoica Merr. BS06 BRIDMONO mi-la-do-ch 508 Connaraceae Agelaea trinervis (Lianus.) Merr. BS06 AGELTRIN me-la-do-ph-li 509 Sapindaceae Xerospermum noronhianum Bl. BS06 XERONORO no-la-do-ph 510 Blechnaceae Stenochlaena palustris Bedd. BS06 STENPALU mi-la-do-fi-hc-li 511 Poaceae Panicum sp. BS06 PANISPP. mi-la-do-pv-hc 512 Euphorbiaceae Endospermum diadenum (miq.) Airy Shaw BS06 ENDODIAD me-la-do-ct-ph 513 Moraceae Ficus variegata Bl. BS06 FICUVARI me-la-do-ct-ph 514 Connaraceae Connarus semidecandrus Jack.. BS06 CONNSEMI mi-la-do-ph-li 515 Fabaceae Archidendron bubalinum (Jack.) Nielsen. BS06 ARCHBUBA mi-la-do-ct-ph 516 Euphorbiaceae Macaranga pruinosa M.A. BS06 MACAPRUI me-la-do-ph 517 Olacaceae Ochanostachys amentacea Mast. BS06 OCHAAMEN no-la-do-ph

42 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 518 Adiantaceae Pityrogramma calomelanos Link. BS06 PITYCALO le-la-do-fi-hc-ad 519 Ulmaceae Trema orientalis (L.) Bl. BS06 TREMORIE mi-la-do-ct-ph 520 Celastraceae Kokoona ochracea (Elmer.) Merr. BS06 KOKOOCHR mi-pe-do-ct-ph 521 Dilleniaceae Dillenia borneensis Hogl. BS06 DILLBORN me-la-do-ph 522 Ulmaceae Gironniera hirta Ridl. BS06 GIROHIRT no-la-do-ph 523 Zingiberaceae Alpinia sp. BS06 ALPISPP. ma-la-do-su-pv-hc-ad 524 Ancistrocladaceae Ancistrocladus tectorius (Lour.) Merr. BS06 ANCITECT me-ve-do-ph-li 525 Moracaceae Ficus grossularioides Burm.f. BS06 FICUGROS me-la-do-ct-ph 526 Asteraceae Blumea balsamifera (L.) DC. BS06 BLUMBALS no-la-do-hc 527 Sapindaceae Mischocarpus pentapetalus (Roxb.) Radlk. BS06 MISCPENT no-la-do-ph 528 Fabaceae Paraserianthes falcataria (l.) fosb. BS06 PARAFALC na-ve-do-ct-ph 529 Fabaceae Paraserianthes falcataria (L.) I. Nielsen BS07 PARAFALC na-ve-do-ct-ph 530 Poaceae Cyrtococcum accrescens Stapf BS07 CYRTACCR mi-co-do-hc-ad 531 Asteraceae Mikania cordata (Burm.f.) B.L. Robinson BS07 MIKACORD mi-co-do-hc-li-ad 532 Nephrolepis Group Nephrolepis exaltata (L.) Schott BS07 NEPHEXAL mi-ve-do-fi-hc-ad 533 Verbenaceae Callicarpa longifolia Lam. BS07 CALLLONG no-la-do-ch 534 Poaceae Paspalum conjugatum Berg. BS07 PASPCONJ mi-co-do-pv-hc-ad 535 Melastomataceae Melastoma affine D. Don BS07 MELAAFFI mi-la-do-ch 536 Rubiaceae Mussaenda frondosa Linn. BS07 MUSSFRON no-ve-do-ch 537 Cyperaceae Scleria purpurascens Steud. BS07 SCLEPURP no-co-do-pv-hc 538 Fabaceae Archidendron ellipticum (Blume) I. Nielsen BS07 ARCHELLI no-la-do-ch 539 Euphorbiaceae Croton argyratus Blume BS07 CROTARGY me-pe-do-ct-ph 540 Euphorbiaceae Macaranga gigantea (Rchb.F.& Zoll.) Muell. Arg. BS07 MACAGIGA ma-pe-do-ch 541 Euphorbiaceae Glochidion rubrum Blume BS07 GLOCRUBR mi-la-do-ch 542 Meliaceae Dysoxylum cf. alliaceum Blume BS07 DYSOALLI me-ve-do-ch 543 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS07 STENPALU mi-la-do-fi-hc-ad-ep 544 Connaraceae Cnestis platantha Griff. BS07 CNESPLAN na-ve-do-ph-li 545 Rubiaceae Neonauclea obtusa (Bl.) Merrill BS07 NEONOBTU me-la-do-ch 546 Euphorbiaceae Sapium baccatum Roxb. BS07 SAPIBACC mi-la-do-ch 547 Fabaceae Fordia johorensis T.C. Whitm. BS07 FORDJOHO mi-la-do-ch 548 Poaceae Ottochloa nodosa (Kunth) Dandy BS07 OTTONODO mi-la-do-pv-hc-ad 549 Asteraceae Mikania cordata (burm.f.) b.l. robinson BS07 MIKACORD no-la-do-hc-li-ad 550 Poaceae Imperata cylindrica (l.) beauv. BS07 IMPECYLI me-ve-do-hc-ad 551 Dilleniaceae Tetracera akara (burm.f.) merrill BS07 TETRAKAR no-la-do-ph-li-ad 552 Rubiaceae Uncaria glabrata (Blume) DC. BS07 UNCAGLAB mi-la-do-ph-li 553 Amaryllidaceae Curculigo villosa (Kurz) Wall. ex Merrill BS07 CURCVILO no-ve-do-de-pv-cr 554 Ulmaceae Trema orientalis (L.) Blume BS07 TREMORIE no-ve-do-ch 555 Asteraceae Erigeron sumatransis Retz. BS07 ERIGSUMA na-la-do-hc 556 Asteraceae Blumea balsamifera (L.) Blume BS07 BLUMBALS no-ve-do-hc 557 Moraceae Ficus variegata Blume BS07 FICUVARI no-co-do-ct-ph 558 Ulmaceae Trema orientalis (L.) Blume BS07 TREMORIE no-la-do-ct-ph 559 Euphorbiaceae Galearia filiformis (Bl.) Pax BS07 GALEFILI no-la-do-ch 560 Convolvulaceae Merremia cf. peltata (L.) Merrill BS07 MERRPELT me-ve-do-hc-li 561 Leeaceae Leea indica (Burm.f.) Merr. BS07 LEEAINDI me-la-do-ch 562 Celastraceae Salacia macrophylla Blume BS07 SALAMACR pl-pe-do-ch 563 Fabaceae Mezoneurum sp. BS07 MEZOSPP. le-la-do-hc-li 564 Fabaceae Phanera pyrrhaneura Benth. BS07 PHANPYRR me-la-do-ph-li 565 Poaceae Panicum incomtum Trin. BS07 PANIINCO me-la-do-pv-hc-ad 566 Myrsinaceae Embelia ribes Burm. F. BS07 EMBERIBE me-la-do-ph-li 567 Annonaceae Uvaria sp. BS07 UVARSPP. me-ve-do-ph-li 568 Dilleniaceae Tetracera indica (Houtt. et Christm. & Panz.) Merrill BS07 TETRINDI me-co-do-ph-li 569 Annonaceae Desmos chinensis Lour. BS07 DESMCHIN me-ve-do-ph-li

43 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 570 Vitaceae Cissus adnata Roxb. BS07 CISSADNA me-ve-do-ph-li 571 Fabaceae Milletia sericea (Vent.) Wight & Arn. BS07 MILLSERI me-ve-do-ph-li 572 Icacinaceae Iodes cirrhosa Turcz. BS07 LODECIRR me-la-do-hc-li-ad 573 Euphorbiaceae Croton caudatus Geisel. BS07 CROTCAUD me-la-do-ch 574 Ancistrocladiaceae Ancistrocladus tectorius (Lour.) Merr. BS07 ANCITECT me-ve-do-ch 575 Euphorbiaceae Macaranga hypoleuca (Rchb.F. & Zoll.) Muell. Arg. BS07 MACAHYPO me-ve-do-ch 576 Ebenaceae Dyospyros siamang Bakh. BS07 DYOSSIAM me-ve-do-ph-li 577 Euphorbiaceae Hevea brasiliensis (Willd. ex A Juss.) M.A. BS08 HEVEBRAS no-ve-do-ct-ph 578 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS08 STECPALU no-la-do-fi-hc-ad 579 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS08 STECPALU no-la-do-hc-li-ad 580 Fabaceae Fordia johorensis T.C. Whitm. BS08 FORDJOHO mi-la-do-ch 581 Gleicheniaceae Dicranopteris linearis (Burm.f.) Underw. BS08 DICRLINE na-la-do-fi-hc-li-ad 582 Rhamnaceae Ventilago oblongifolia Blume BS08 VENTOBLO no-ve-do-ph-li 583 Thymelaeaceae Enkleia malaccensis Griff. BS08 ENKLMALA mi-la-do-ph-li 584 Connaraceae Agelaea trinervis (Lianos) Merr. BS08 AGELTRIN mi-la-do-ph-li 585 Rubiaceae Coffea canephora Pierre var. robusta (L.) Cheval BS08 COFFCANE me-la-do-ch 586 Menispermaceae Limacia scandens Lour. BS08 LIMASCAN no-la-do-ph-li 587 Fabaceae Derris sp1. BS08 DERRSPP1 mi-ve-do-ph-li 588 Poaceae Axonopus compressus (Swartz) Beauv. BS08 AXONCOMP no-ve-do-pv-hc-ad 589 Asclepiadaceae Telosma accesdens (Blume) Backer BS08 TELOACCE no-la-do-ph-li 590 Nephrolepis Group Nephrolepis exaltata (L.) Schott BS08 NEPHEXAL na-la-do-fi-hc-ad 591 Rubiaceae Gardenia anisophylla Jack ex Roxb. BS08 GARDANIS me-la-do-ch 592 Liliaceae Dracaena elliptica Thunb. BS08 DRACELLI no-la-do-pv-ch 593 Piperaceae Piper ungaramense DC. BS08 PIPEUNGA no-ve-do-su-hc-li-ad-ep 594 Passifloraceae Adenia macrophylla (Bl.) Kds. BS08 ADENMACR no-ve-do-hc-li 595 Adiantum Group Taenitis blechnoides (Willd.) Sw. BS08 TAENBLEC no-la-do-fi-hc 596 Poaceae Centotheca lappacea (L.) Desvaux BS08 CENTLAPP mi-la-do-pv-hc-ad 597 Poaceae Paspalum conjugatum Berg. BS08 PASPCONJ no-co-do-pv-hc-ad 598 Poaceae Imperata cylindrica (L.) Beauv. BS08 IMPECYLI me-co-do-pv-hc-ad 599 Myrtaceae Syzygyum suringarianum (Koord. & Valeton) Amshoff BS08 SYZYSURI me-la-do-hc-ad 600 Tectaria Group Tectaria singaporeana (Wall. ex Hook. & Grev.) Copel BS08 TECTSING me-ve-do-pv-hc-ad 601 Apocynaceae Willughbeia sp. BS08 WILLSPP. mi-co-do-ph-li 602 Dilleniaceae Dillenia ovata Wall. ex Hook .f. & Thoms. BS08 DILLOVAT me-la-do-ch 603 Apocynaceae Willughbeia coriacea Wall. BS08 WILLCORI no-la-do-ph-li 604 Poaceae Ottochloa nodosa (Kunth) Dandy BS08 OTTONODO mi-co-do-pv-hc-ad 605 Annonaceae Uvaria littoralis (Blume) Blume BS08 UVARLITT no-la-do-ph-li 606 Euphorbiaceae Glochidion rubrum Blume BS08 GLOCRUBR mi-la-do-ch 607 Euphorbiaceae Macaranga trichocarpa (Rchb.f. & Zoll.) Muell. Arg. BS08 MACATRIC me-la-do-ch 608 Euphorbiaceae Galearia filiformis (Bl.) Pax BS08 GALEFILI me-la-do-ch 609 Zingiberaceae Hornstedtia sp. BS08 HORNSPP. me-la-do-su-hc-ad 610 Dilleniaceae Tetracera scandens (L.) Merrill BS08 TETRSCAN no-ve-do-ph-li-ad 611 Myrtaceae Syzygium lineatum (DC.) Merrill & Perry BS08 SYZYLINE na-la-do-ph 612 Fabaceae Phanera kockiana Benth. BS08 PHANKOCK no-ve-do-ph-li 613 Euphorbiaceae Mallotus affinis Merrill BS08 MALLAFFI me-la-do-ch 614 Moraceae Ficus padana Burm.f. BS08 FICUPADA me-la-do-ch 615 Olacaceae Ochanostachys amentacea Mast. BS08 OCHAAMEN me-la-do-ch 616 Rubiaceae Mussaenda frondosa Linn. BS08 MUSSFRON no-la-do-ph-li 617 Moraceae Ficus variegata Blume BS08 FICUVARI me-la-do-ph-ad 618 Rhizophoraceae Gynotroches axillaris Blume BS08 GYNOAXIL no-la-do-ch 619 Clusiaceae Garcinia dioica Blume BS08 GARCDIOI no-la-do-ch 620 Euphorbiaceae Macaranga gigantea (Rchb.f. & Zoll.) Muell. Arg. BS08 MACAGIGA ma-la-do-ph-ad 621 Cyperaceae Scleria levis Retz. BS08 SCLELEVI mi-la-do-pv-hc

44 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 622 Verbenaceae Clerodendron fragans (Vent.) Willd. BS08 CLERFRAG me-la-do-ch 623 Fabaceae Indet BS08 INDET*** me-ve-do-ch 624 Myristicaceae Knema latericia Elmer BS08 KNEMLATE me-la-do-ch 625 Vitaceae Nothocissus spicifera (Griff.) A. Latif BS08 NOTHSPIC me-la-do-ph-li 626 Annonaceae Melodorum kentii Hook.f. & Thoms. BS08 MELOKENT mi-la-do-ph-li 627 Verbenaceae Premna sp. BS08 PREMSPP. no-la-do-ph-li 628 Ancistrocladaceae Ancistrocladus tectorius (Lour.) Merr. BS08 ANCITECT me-co-do-ch 629 Melastomataceae Melastoma affine D. Don BS08 MELAAFFI mi-la-do-ch 630 Icacinaceae Gonocaryum littorale (Bl.) Pax BS08 GONOLITT me-la-do-ch 631 Euphorbiaceae Antidesma stipulare Blume BS08 ANTISTIP no-la-do-ch 632 Ulmaceae Gironniera nervosa Planch BS08 GIRONERV me-ve-do-ct-ph 633 Euphorbiaceae Galearia filiformis (Bl.) Pax BS08 GALEFILI no-la-do-ct-ph 634 Rubiaceae Uncaria sp. BS08 UNCASPP. me-la-do-ph-li 635 Rubiaceae Chasalia curviflora (Wall.) Thw. BS08 CHASCURV no-la-do-ch 636 Fabaceae Archidendron jiringa (Jack) I. Nielsen BS08 ARCHJIRI mi-la-do-ch 637 Sapindaceae Nephelium uncinatum Radlk ex P.W. Leenhouts BS08 NEPHUNCI no-ve-do-ch 638 Fabaceae Derris sp2. BS08 DERRSPP2 no-la-do-ph-li 639 Clusiaceae Garcinia dioica Blume BS08 GARCDIOI mi-la-do-ch 640 Myrtaceae Rhodamnia cinerea Jack BS08 RHODCINE mi-la-do-ch 641 Sapotaceae Palaquium gutta (Hook.f.) Baillon BS08 PALAGUTT no-la-do-ch 642 Verbenaceae Callicarpa longifolia Lam. BS08 CALLLONG me-la-do-ch 643 Simaroubaceae Eurycoma longifolia Jack BS08 EURYLONG mi-la-do-ch 644 Connaraceae Agelaea sp. BS08 AGELSPP. me-la-do-ch-li 645 Rubiaceae Gardenia anishophylla jack ex roxb. BS08 GARDANIS me-la-do-ch 646 Euphorbiaceae Macaranga gigantea (Rchb.F. & Zoll.) Muell. Arg. BS09 MACAGIGA ma-la-do-ph-ad 647 Ulmaceae Gironniera nervosa Planch. BS09 GIRONERV me-la-do-ct-ph 648 Melastomataceae Memecylon paniculatum Jack BS09 MEMEPANI mi-la-do-ch 649 Euphorbiaceae Ptychopxis costata Miq. BS09 PTYCCOST me-la-do-ch 650 Clusiaceae Calophyllum soulattri Burm.f. BS09 CALOSOUL me-la-do-ch 651 Fabaceae Phanera sp. BS09 PHANSPP. no-la-do-ph-li 652 Burseraceae Santiria sp. BS09 SANTSPP. no-la-do-ch 653 Fabaceae Phanera kockiana Benth. BS09 PHANKOCK no-la-do-ph-li 654 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS09 STENPALU no-la-do-fi-hc-ad 655 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS09 STENPALU no-la-do-fi-hc-ad-ep 656 Lecythidaceae Barringtonia racemosa (L.) Sprng. BS09 BARRRACE no-la-do-ch 657 Nephrolepis Group Nephrolepis bisserata (Sw.) Schott BS09 NEPHBISS mi-la-do-fi-hc-ad 658 Rubiaceae Mussaenda frondosa Linn. BS09 MUSSFRON no-la-do-ph-li 659 Vitaceae Nothocissus spicifera (Griff.) A. Latif BS09 NOTHSPIC me-la-do-ph-li 660 Fabaceae Fordia johorensis T.C. Whitm. BS09 FORDJOHO mi-la-do-ch 661 Euphorbiaceae Hevea brasiliensis (Willd. ex A. Juss.) M.A. BS09 HEVEBRAS no-la-do-ct-ph 662 Euphorbiaceae Hevea brasiliensis (Willd. ex A. Juss.) M.A. BS09 HEVEBRAS mi-co-do-ct-ph 663 Euphorbiaceae Koilodepas brevipes Merrill BS09 KOILBREV no-la-do-ch 664 Fabaceae Derris sp2. BS09 DERRSPP2 no-la-do-ph-li 665 Menispermaceae Fibraurea cf. chloroleuca Miers BS09 FIBRCHLO me-la-do-ph-li 666 Melastomataceae Pachycentria constricta (Blume) Blume BS09 PACHCONS no-la-do-su-hc-li-ad-ep 667 Annonaceae Uvaria macrophylla Roxb. BS09 UVARMACR me-la-do-ph-li 668 Ancistocladaceae Ancistrocladus tectorius (Lour.) Merr. BS09 ANCITECT me-la-do-ch 669 Connaraceae Agelaea trinervis (Lianos) Merr. BS09 AGELTRIN me-la-do-ph-li 670 Euphorbiaceae Glochidion philippicum (Cav.) C.B. Rob. BS09 GLOCPHIL no-la-do-ch 671 Poaceae Centhoteca lappacea (L.) Desvaux BS09 CENTLAPP mi-la-do-pv-hc 672 Euphorbiaceae Indet BS09 INDET*** me-la-do-ph 673 Gleicheniaceae Dicranopteris linearis (Burm.f.) Underw. BS09 DICRLINE na-la-do-fi-hc-li-ad

45 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 674 Poaceae Imperata cylindrica (L.) Beauv. BS09 IMPECYLI me-ve-do-pv-hc-ad 675 Euphorbiaceae Galearia filiformis (Bl.) Pax BS09 GALEFILI me-la-do-ch 676 Fabaceae Fordia johorensis T.C. Whitm. BS09 FORDJOHO me-ve-do-ct-ph 677 Cyperaceae Scleria purpurascens Steud BS09 SCLEPURP me-co-do-hc-ad 678 Myrsinaceae Labisia pumila (Blume) f. Vill. BS09 LABIPUMI me-co-do-su-hc-ad 679 Rubiaceae Gardenia forsteniana Miq. BS09 GARDFORS me-la-do-ch 680 Apocynaceae Urceola brahysepala Hookf. BS09 URCEBRAH me-la-do-ph-li 681 Rhizophoraceae Gynotroches axillaris Blume BS09 GYNOAXIL no-la-do-ct-ph-ad 682 Fabaceae Archidendron jiringa (Jack) I. Nielsen BS09 ARCHJIRI me-la-do-ct-ph 683 Melastomataceae Melastoma affine D.Don BS09 MELAAFFI mi-la-do-hc 684 Ebenaceae Diospyros malam Bakh. BS09 DIOSMALA me-ve-do-ch 685 Poaceae Panicum sp. BS09 PANISPP. mi-la-do-pv-hc-ad 686 Fabaceae Cassia sp. BS09 CASSSPP. mi-la-do-ch 687 Euphorbiaceae Homalanthus populneus (Grisel.) Pax BS09 HOMAPOPU no-la-do-ch 688 Euphorbiaceae Macaranga gigantea (Rchb.F. & Zoll.) Muell. Arg. BS09 MACAGIGA me-la-do-ch 689 Verbenaceae Clerodendron deflexum Wall BS09 CLERDEFL me-la-do-ph-li 690 Tectaria Group Tectaria singaporeana (Wall. ex Hook.& Grev.) Copel BS09 TECTSING me-ve-do-fi-hc-ad 691 Rutaceae Euodia macrocarpa King BS09 EUODMACR me-la-do-ct-ph 692 Sapindaceae Nephelium uncinatum Radlk ex P.W. Leenhouts BS09 NEPHUNCI mi-la-do-ch 693 Fabaceae Derris sp1. BS09 DERRSPP1 no-la-do-ph-li 694 Rubiaceae Coffea canephora Pierre (Linden ex De Wildem.) Cheval BS09 COFFCANE me-la-do-ch 695 Piperaceae Piper ungaramense DC. BS09 PIPEUNGA no-ve-do-su-hc-li-ad-ep 696 Dilleniaceae Tetracera scandens (L.) Merrill BS09 TETRSCAN me-la-do-ph-li 697 Anacardiaceae Melanochyla caesia (Bl.) Ding Hou BS09 MELACAES me-la-do-ch 698 Davallia Group Davallia solida (Forst.) Sw. BS09 DAVASOLI na-la-do-fi-hc-ad-ep 699 Schizaeaceae Lygodium circinnatum (Burm.f.) Sw. BS09 LYGOCIRC mi-la-do-fi-hc-li 700 Euphorbiaceae Aporusa lucida (Miq.) Airy Shaw BS09 APORLUCI me-la-do-ch 701 Myrsinaceae Embelia sp. BS09 EMBESPP. mi-la-do-ph-li 702 Connaraceae Rourea sp. BS09 ROURSPP. na-la-do-ph-li 703 Verbenaceae Vitex pinnata Linn. BS09 VITEPINN no-la-do-ch 704 Euphorbiaceae Sapium baccatum Roxb. BS10 SAPIBACC no-co-do-ph 705 Clusiaceae Cratoxylum sumatranum (Jack) Blume BS10 CRATSUMA me-la-do-ct-ph 706 Clusiaceae Calophyllum molle King BS10 CALOMOLL me-la-do-ct-ph 707 Moraceae Ficus ribes Reinw. BS10 FICURIBE me-la-do-ph 708 Elaeocarpaceae Elaeocarpus stipularis Blume BS10 ELAESTIP no-la-do-ct-ph 709 Piperaceae Piper caninum Blume BS10 PIPECANI mi-pe-do-su-hc-li-ad-ep 710 Fabaceae Milletia sericea (Vent.) Wight & Arn. BS10 MILLSERI me-la-do-ph-li 711 Rubiaceae Ixora sp. BS10 IXORSPP. no-la-do-ch 712 Euphorbiaceae Aporusa dioica (Roxb.) M.A. BS10 APORDIOI no-la-do-ct-ph 713 Loganaceae Fagraea recemosa Jack ex Wall. BS10 FAGRRECE me-la-do-ct-ph 714 Myristicaceae Knema laurina (Bl.) Warb. BS10 KNEMLAUR me-la-do-ct-ph 715 Euphorbiaceae Glochidion arborescens Blume BS10 GLOCARBO no-la-do-ct-ph 716 Nephrolepis Group Nephrolepis exaltata (L.) Schott BS10 NEPHEXAL mi-la-do-fi-hc-ad 717 Vitaceae Tetrastigma papillosum (Blume) Planch. BS10 TETRPAPI me-la-do-ph-li-ad 718 Fabaceae Sphatolobus ferrugineus (Zoll. & Mor.) Benth. BS10 0 me-la-do-ph-li 719 Lauraceae Lindera lucida (Bl.) Boerl. BS10 LINDLUCI me-la-do-ct-ph 720 Verbenaceae Clerodendrum fragans (Vent.) Willd. BS10 CLERFRAG me-la-do-ph-li 721 Rhizophoraceae Carallia brachiata (Lour.) Merrill BS10 CARABRAC no-la-do-ct-ph 722 Poaceae Ottochloa nodosa (Kunth) Dandy BS10 OTTONODO mi-la-do-pv-hc-ad 723 Poaceae Oplismenus compositus (L.) Beauv. BS10 OPLICOMP mi-la-do-pv-hc-ad 724 Poaceae Lophaterum gracile Brongn. BS10 LOPHGRAC mi-la-do-pv-hc-ad 725 Euphorbiaceae Macaranga hypoleuca (Rchb.F. & Zoll.) Muell. Arg. BS10 MACAHYPO me-la-do-ct-ph

46 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 726 Euphorbiaceae Bischofia javanica Blume BS10 BISCJAVA no-la-do-de-ph 727 Connaraceae Cnestis palala (Lour.) Merr. BS10 CNESPALA na-la-do-ph-li 728 Rubiaceae Psychotria viridiflora Reinw. ex Blume BS10 PSYCVIRI no-la-do-ch 729 Poaceae Centhotheca lappacea (L.) Desvaux BS10 CENTLAPP mi-la-do-hc 730 Poaceae Ottochloa nodosa (Kunth) Dandy BS10 OTTONODO mi-la-do-hc 731 Euphorbiaceae Macaranga gigantea (Rchb.F. & Zoll.) Muell. Arg. BS10 MACAGIGA ma-la-do-ph 732 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS10 STENPALU no-la-do-hc-li-ad-ep 733 Gleicheniaceae Dicranopteris linearis (Burm.f.) Underw. BS10 DICRLINE na-la-do-fi-hc-li-ad 734 Dilleniaceae Tetracera scandens (L.) Merrill BS10 TETRSCAN me-ve-do-ph-li 735 Commelinaceae Forrestia mollisima (Bl.) Kds. BS10 FORRMOLL me-la-do-ro-su-hc-ad 736 Schizaeaceae Lygodium circinnatum (Burm.f.) Sw. BS10 LYGOCIRC mi-la-do-ph-li 737 Theaceae Eurya acuminata DC. BS10 EURYACUM no-la-do-ct-ph 738 Meliaceae Aglaia ganggo Miq. BS10 AGLAGANG me-ve-do-ct-ph 739 Melastomataceae Clidemia hirta (L.) D.Don BS10 CLIDHIRT no-la-do-ch 740 Euphorbiaceae Croton argyratus Blume BS10 CROTARGY me-la-do-ct-ph 741 Verbenaceae Clerodendrum laevifolium Blume BS10 CLERLAEV mi-la-do-ct-ph 742 Lauraceae Litsea resinosa Blume BS10 LITSRESI me-la-do-ct-ph 743 Fabaceae Koompassia malaccensis Maing. ex Benth. BS10 KOOMMALA mi-la-do-ch 744 Apocynaceae Urceola sp. BS10 URCESPP. no-la-do-ph-li 745 Euphorbiaceae Hevea brasiliensis (Willd. ex. A. Juss.) M.A. BS10 HEVEBRAS me-la-do-ct-ph 746 Styracaceae Styrax benzoin Dryand BS10 STYRBENZ no-la-do-ch 747 Poaceae Panicum sp. BS10 PANISPP. na-la-do-pv-hc-ad 748 Lauraceae Litsea cf. noronhae Blume BS10 LITSNORO me-la-do-ph 749 Burseraceae Canarium littorale Blume BS10 CANALITT no-la-do-ch 750 Tiliaceae Grewia acuminata Juss. BS10 GREWACUM mi-la-do-ch 751 Linaceae Ixonanthes sp. BS10 IXONSPP. mi-la-do-ch 752 Gnetaceae Gnetum latifolium Blume BS10 GNETLATI no-la-do-ph-li-ep 753 Rutaceae Euodia pilulifera King BS10 EUODPILU pl-la-do-ct-ph 754 Menispermaceae Pericampylus glaucus Merrill BS10 PERIGLAU me-la-do-ph-li-ep 755 Sapindaceae Arytera xerocarpa (Blume) Adelb. BS10 ARYTXERO me-la-do-ph 756 Araceae Scindapsus parakensis Hook.f. BS10 SCINPARA me-la-do-su-hc-li-ad-ep 757 Euphorbiaceae Drypetes sp1. BS10 DRYPSPP1 no-la-do-ph 758 Euphorbiaceae Drypetes sp2. BS10 DRYPSPP2 no-la-do-ct-ph 759 Piperaceae Piper baccatum Blume BS10 PIPEBACC no-la-do-su-hc-li-ad-ep 760 Myrtaceae Syzygium polyanthum (Wight) Walp. BS10 SYZYPOLY no-la-do-ch 761 Leeaceae Leea indica (Burm.f) Merr. BS10 LEEAINDI me-la-do-ch 762 Moraceae Ficus obscura Blume BS10 FICUOBSC me-la-do-ch 763 Polygalaceae Xanthophyllum sp1. BS10 XANTSPP1 me-la-do-ch 764 Aspleniaceae Asplenium nidus Linn. BS10 ASPLNIDU ma-co-do-fi-hc-ep 765 Rubiaceae Neonauclea obtusa (Bl.) Merrill BS10 NEONOBTU pl-la-do-ct-ph 766 Menispermaceae Anamirta cocculus Wight & Arn BS10 ANAMCOCC me-la-do-ph-li 767 Myrtaceae Syzygium lineatum (DC.) Merrill & Perry BS10 SYZYLINE mi-la-do-ct-ph 768 Lauraceae Cryptocarya ferea Blume BS10 CRYPFERE no-la-do-ct-ph 769 Melastomataceae Memecylon paniculatum Jack BS10 MEMEPANI mi-la-do-ch 770 Olacaceae Strombosia javanica Blume BS10 STROJAVA me-la-do-ct-ph 771 Polypodiaceae Drynaria sparsiosora (Desv.) Moore BS10 DRYNSPAR pl-ve-do-fi-hc-ad-ep 772 Dioscoreaceae Dioscorea hispida Dennst. BS10 DIOSHISP no-la-do-de-cr 773 Asclepiadaceae Hoya macrophylla Blume BS10 HOYAMACR me-la-do-su-hc-li-ad-ep 774 Cyperaceae Scleria levis Retz. BS10 SCLELEVI mi-co-do-pv-hc-ad 775 Zingiberaceae Hornstedtia sp. BS10 HORNSPP. me-la-do-su-hc-ad 776 Anacardiaceae Pentaspadon motleyi Hook.f. BS10 PENTMOTL mi-la-do-ct-ph 777 Sapotaceae Palaquium cf. obovatum (Griff.) Engl. BS10 PALAOBOV me-la-do-ph

47 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 778 Meliaceae Chisocheton divergens Blume BS10 CHISDIVE me-la-do-ch 779 Annonaceae Artabotrys suaveolens Blume BS10 ARTASUAV no-la-do-ch 780 Meliaceae Aglaia sp. BS10 AGLASPP. mi-la-do-ch 781 Poaceae Leptaspis urceolata (Roxb.) R.Br. BS10 LEPTURCE no-ve-do-pv-hc-ad 782 Moraceae Ficus sagittata Vahl. BS10 FICUSAGI me-la-do-hc-li 783 Rutaceae Micromelum minutum (Forst.f.) Wight. & Arn. BS10 MICRMINU mi-la-do-ph 784 Burseraceae Santiria oblongifolia Blume BS10 SANTOBLO me-la-do-ct-ph-ad 785 Myrtaceae Rhodamnia cinerea Jack BS10 RHODCINE no-la-do-ph 786 Burseraceae Canarium sp. BS10 CANASPP. me-la-do-ch 787 Ulmaceae Gironniera nervosa Planch. BS10 GIRONERV me-la-do-ct-ph 788 Flacourtiaceae Flacourtia rukam Zoll. & Mor. BS10 FLACRUKA no-la-do-ch 789 Moraceae Artocarpus lanceafolia Roxb. BS10 ARTOLANC pl-la-do-ct-ph 790 Liliaceae Dracaena elliptica Thunb. BS10 DRACELLI me-la-do-ro-su-ch 791 Euphorbiaceae Baccaurea cf. lanceolata Muell.Arg. BS10 BACCLANC pl-la-do-ct-ph 792 Rubiaceae Randia spinosa (L.f.) Poir. BS10 RANDSPIN mi-la-do-ch 793 Burseraceae Santiria tomentosa Blume BS10 SANTTOME me-ve-do-ct-ph 794 Zingiberaceae Zingiber zerumbet (L.) Sm. BS10 ZINGZERU me-la-do-su-pv-hc-ad 795 Euphorbiaceae Bridelia minutiflora Hook.f BS10 BRIDMINU me-la-do-ct-ph 796 Sapindaceae Pometia pinnata J.R. Forster & D. Forster BS10 POMEPINN me-la-do-ph 797 Verbenaceae Lantana camara Linn. BS10 LANTCAMA mi-la-do-ch 798 Alangiaceae Alangium villosum Wangerin BS10 ALANVILL no-la-do-ct-ph 799 Rubiaceae Indet 1 (empty) BS10 INDE(EMP no-la-do-ph-li 800 Meliaceae Dysoxylum arborescens (Bl.) Miq. BS10 DYSOARBO no-la-do-ch 801 Asclepiadaceae Telosma accesdens (Blume) Backer BS10 TELOACCE mi-la-do-ph-li 802 Annonaceae Desmos chinensis Lour. BS10 DESMCHIN no-la-do-ch 803 Annonaceae Fissitigma latifolium (Dun.) Merrill BS10 FISSLATI me-la-do-ch 804 Rutaceae Luvunga sarmentosa Kurz BS10 LUVUSARM me-la-do-ch 805 Vitaceae Cayratia pedata (Lour.) Juss. BS10 CAYRPEDA no-la-do-ph-li 806 Moraceae Artocarpus dadah Miq. BS10 ARTODADA me-la-do-ct-ph 807 Elaeocarpaceae Elaeocarpus stipularis Blume BS10 ELAESTIP no-co-do-ct-ph-ad 808 Piperaceae Piper caninum Blume BS10 PIPECANI no-la-do-su-hc-li-ad-ep 809 Sterculiaceae Sterculia subpeltata Blume BS10 STERSUBP pl-la-do-ph 810 Poaceae Panicum incomtum Trin. BS10 PANIINCO me-la-do-pv-hc-li 811 Annonaceae Uvaria purpurea Blume BS10 UVARPURP me-la-do-ct-ph 812 Apocynaceae Alstonia scholaris R.Br. BS10 ALSTSCHO me-la-do-ct-ph 813 Apocynaceae Voacanga foetida Rolfe BS10 VOACFOET me-la-do-ct-ph 814 Rubiaceae Uncaria glabrata (Blume) DC. BS10 UNCAGLAB me-la-do-ph-li 815 Dilleniaceae Dillenia sp. BS10 DILLSPP. pl-la-do-ch 816 Euphorbiaceae Baccaurea motleyana Muell.Arg. BS10 BACCMOTL pl-la-do-ch 817 Arecaceae Calamus sp. BS10 CALASPP. me-la-do-ro-pv-hc-li 818 Fabaceae Indet 2 (empty) BS10 INDE(EMP me-la-do-ph-li 819 Euphorbiaceae Macaranga gigantea (Rchb.F. & Zoll.) Muell. Arg. BS11 MACAGIGA pl-pe-do-ph 820 Pteris Group Stenochlaena palustris (Burm.) Bedd. BS11 STENPALU no-la-do-fi-ph-li-ad-ep 821 Piperaceae Piper sp. BS11 PIPESPP. me-pe-do-su-hc-li-ad-ep 822 Lauraceae Litsea elliptica Blume BS11 LITSELLI me-la-do-ch 823 Euphorbiaceae Glochidion rubrum Blume BS11 GLOCRUBR no-la-do-ch 824 Connaraceae Rourea mimosoides (Vahl.) Planch. BS11 ROURMIMO na-pe-do-ph-li 825 Euphorbiaceae Glochidion rubrum Blume BS11 GLOCRUBR mi-la-do-ct-ph 826 Cyperaceae Scleria purpurascens Steud. BS11 SCLEPURP mi-co-do-pv-hc 827 Annonaceae Desmos chinensis Lour. BS11 DESMCHIN no-la-do-ch 828 Rubiaceae Psycothria viridiflora Reinw. ex Blume BS11 PSYCVIRI no-la-do-ch 829 Myrtaceae Syzygium fastigiatum (Blume) Merrill & Perry BS11 SYZYFAST me-la-do-ct-ph

48 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 830 Connaracae Rourea sp. BS11 ROURSPP. na-la-do-ph-li 831 Euphorbiaceae Aporusa dioica (Roxb.) M.A. BS11 APORDIOI no-co-do-ch 832 Theaceae Eurya acuminata DC. BS11 EURYACUM mi-la-do-ch 833 Rubiaceae Ixora javanica (Bl.) DC. BS11 IXORJAVA me-la-do-ch 834 Menispermaceae Fibraurea chloroleuca Miers BS11 FIBRCHLO me-la-do-ph-li 835 Annonaceae Artabotrys suaveolens Blume BS11 ARTASUAV no-la-do-ch 836 Dilleniaceae Tetracera scandens (L.) Merrill BS11 TETRSCAN me-la-do-hc-li 837 Myrtaceae Syzygium lineatum (DC.) Merrill & Perry BS11 SYZYLINE mi-la-do-ct-ph 838 Myrtaceae Rhodamnia cinerea Jack BS11 RHODLINE no-la-do-ph 839 Araliaceae Polyscias nodosa (Blume) Seem. BS11 POLYNODO me-la-do-ct-ph 840 Menispermaceae Pericampylus glaucus Merrill BS11 PERIGLAU me-la-do-cr-li-ep 841 Rubiaceae Urophyllum sp. BS11 UROPSPP. me-la-do-ct-ph 842 Melastomataceae Memecylon paniculatum Jack BS11 MEMEPANI no-la-do-ch 843 Poaceae Lophaterum gracile Brongn. BS11 LOPHGRAC no-la-do-pv-hc-ad 844 Rhizophoraceae Carralia brachiata (Lour.) Merrill BS11 CARRBRAC mi-la-do-ct-ph 845 Myrtaceae Syzygium polyanthum (Wight) Walp BS11 SYZYPOLY no-la-do-ch 846 Rubiaceae Geophylla repens (L.) I.M.Johston BS11 GEOPREPE mi-la-do-su-hc-li 847 Poaceae Ottochloa nodosa (Kunth) Dandy BS11 OTTONODO mi-la-do-pv-hc-ad 848 Rutaceae Luvunga sarmentosa Kurz BS11 LUVUSARM me-la-do-ct-ph-li 849 Myristicaceae Knema sp1. BS11 KNEMSPP1 no-la-do-ch 850 Apocynaceae Willughbeia coriacea Wall. BS11 WILLCORI me-la-do-ph-li 851 Fabaceae Spatholobus ferrugineus (Zoll. & Mor.) Benth. BS11 SPATFERR me-la-do-ph-li 852 Meliaceae Dysoxylum sp. BS11 DYSOSPP. mi-la-do-ch 853 Fabaceae Milletia sericea (Vemt.) Wight & Arn. BS11 MILLSERI mi-la-do-ph-li 854 Celastraceae Salacia sp. BS11 SALASPP. me-la-do-ph-li 855 Rutaceae Micromelum minutum (Forst.f.) Wight. & Arn. BS11 MICRMINU mi-la-do-ch 856 Vitaceae Tetrastigma papillosum (Blume) Planch. BS11 TETRPAPI no-la-do-su-hc-li-ad-ep 857 Polypodiaceae Drynaria sparsiosora (Desv.) Moore BS11 DRYASPAR pl-ve-do-fi-hc-ad-ep 858 Tiliaceae Grewia acuminata Juss. BS11 GREWACUM no-la-do-ch 859 Myristicaceae Knema sp2. BS11 KNEMSPP2 me-la-do-ct-ph 860 Myrtaceae Syzygium confertum (Korth.) Merrill & Perry BS11 SYZYCONF no-la-do-ch 861 Euphorbiaceae Bridelia minutiflora Hook.f. BS11 BRIDMINU me-pe-do-ch 862 Meliaceae Aglaia dookoo Griff. BS11 AGLADOOK me-la-do-ch 863 Verbenaceae Clerodendrum laevifolium Blume BS11 CLERLAEV me-la-do-ch 864 Euphorbiaceae Hevea brasiliensis (Willd. ex A. Juss.) M.A. BS11 HEVEBRAS no-co-do-ct-ph 865 Burseraceae Canarium sp. BS11 CANASPP. mi-la-do-ch 866 Meliaceae Dysoxylum arborescens (Bl.) miq. BS11 DYSOARBO mi-la-do-ch 867 Moraceae Ficus variegata Blume BS11 FICUVARI me-co-do-ct-ph 868 Meliaceae Aglaia ganggo Miq. BS11 AGLAGANG me-la-do-ct-ph 869 Icacinaceae Platea excelsa Blume BS11 PLATEXCE pl-la-do-ph 870 Cyperaceae Cyperus diffusus Vahl BS11 CYPEDIFF mi-co-do-ro-pv-hc 871 Apocynaceae Urceola brahysepala Hook.f. BS11 URCEBRAH me-la-do-ph-li 872 Lecythidaceae Barringtonia macrostachya (Jack) Kurz BS11 BARRMACR me-la-do-ct-ph 873 Rubiaceae Morinda citrifolia Linn. BS11 MORICRTR no-la-do-ph-li-ep 874 Liliaceae Dracaena elliptica Thunb. BS11 DRACELLI no-la-do-pv-ch 875 Nephrolepis Group Nephrolepis exaltata (L.) Scott BS11 NEPHEXAL na-la-do-fi-hc-ad 876 Elaeocarpaceae Elaeocarpus stipularis Blume BS11 ELAESTIP me-la-do-ct-ph 877 Annonaceae Uvaria purpurea Blume BS11 UVARPURP me-la-do-ph-li 878 Olacaceae Strombosia javanica Blume BS11 STROJAVA me-la-do-ct-ph 879 Melastomataceae Pternandra caerulescens Jack BS11 PTERCAER no-la-do-ph 880 Myristicaceae Horsfieldia glabra (Bl.) Warb. BS11 HORSGLAB me-la-do-ph 881 Rubiaceae Randia spinosa (L.f.) Poir. BS11 RANDSPIN no-la-do-ch

49 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 882 Symplocaceae Symplocos sp. BS11 SYMPSPP. mi-la-do-ch 883 Euphorbiaceae Mallotus sp. BS11 MALLSPP. me-la-do-ch 884 Adiantum Group Taenitis blechnoides (Willd.) Sw. BS11 TAENBLEC mi-la-do-fi-hc-ad 885 Zingiberaceae Hornstedtia sp. BS11 HORNSPP. me-la-do-su-hc-ad 886 Melastomataceae Clidemia hirta (L.) D.Don BS11 CLIDHIRT no-la-do-ch 887 Myristicaceae Gymnacranthera forbesii (King) Warb. BS11 GYMNFORB no-la-do-ch 888 Dilleniaceae Dillenia ovata Wall. ex Hook.f. & Thoms. BS11 DILLOVAT me-la-do-ch 889 Connaraceae Agelaea trinervis (Lianos) Merr. BS11 AGELTRIN me-la-do-ph-li 890 Fabaceae Koompasia malaccensis Maing. ex Benth. BS11 KOOMMALA no-la-do-ch 891 Annonaceae Polyalthia rumphii (Bl.) Merrill BS11 POLYRUMP me-la-do-ch 892 Burseraceae Dacryodes rostrata (Blume) H.J. Lam BS11 DACRROST me-la-do-ch 893 Rhamnaceae Zizyphus calophylla Wall. ex Hk.f. BS11 ZIZYCALO no-la-do-ph-li 894 Meliaceae Dysoxylum sp. BS11 DYSOSP. no-la-do-ct-ph 895 Anacardiaceae Pentaspadon motleyi Hook.f. BS11 PENTMOTL me-la-do-ct-ph 896 Annonaceae Xylopia malayana Hook.f. & Thoms. BS11 XYLOMALA no-la-do-ch 897 Araceae Scindapsus parakensis Hook.f. BS11 SCINPARA me-la-do-su-hc-li-ad-ep 898 Polygalaceae Xanthophyllum laevis van der Meijden BS11 XANTLAEV no-la-do-ct-ph 899 Apocynaceae Voacanga foetida Rolfe BS11 VOACFOET me-la-do-ch 900 Clusiaceae Cratoxylum cochinchinense (Lour.) Blume BS11 CRATCOCH no-la-do-ch 901 Poaceae Centotheca lappacea (L.) Desvaux BS11 CENTLAPP mi-la-do-pv-hc-ad 902 Sapindaceae Xerospermum noronhianum Blume BS11 XERONORO me-la-do-ct-ph 903 Fabaceae Archidendron jiringa (Jack) I. Nielsen BS11 ARCHJIRI me-la-do-ct-ph 904 Cyperaceae Hypolytrum nemorum (Vahl.) Spreng. BS11 HYPONEMO pl-co-do-ro-pv-hc-ad 905 Simaroubaceae Eurycoma longifolia Jack BS11 EURYLONG mi-la-do-ch 906 Rubiaceae Timonius timon (Spreng.) Merr. BS11 TIMOTIMO me-la-do-ct-ph 907 Euphorbiaceae Macaranga hypoleuca (Rchb. F. & Zoll.) Muell. Arg. BS11 MACAHYPO me-la-do-ct-ph 908 Euphorbiaceae Galearia filiformis (Bl.) Pax BS11 GALEFILI me-la-do-ct-ph 909 Clusiaceae Garcinia cf. nigrolineata Planch. ex T.A. BS11 GARCNIGR me-la-do-ct-ph 910 Gnetaceae Gnetum latifolium Blume BS11 GNETLATI no-la-do-ph-li 911 Moraceae Artocarpus kemando Miq. BS11 ARTOKEMO no-la-do-ct-ph 912 Styracaceae Styrax benzoin Dryand. BS11 STYRBENZ me-la-do-ch 913 Ulmaceae Gironniera nervosa Planch. BS11 GIRONERV me-la-do-ct-ph 914 Apocynaceae Alstonia scholaris R.Br. BS11 ALSTSCHO no-la-do-ch 915 Vitaceae Cayratia pedata (Lour.) Juss. BS11 CAYRPEDA me-la-do-su-hc-li-ad-ep 916 Cyperaceae Scleria levis Retz. BS11 SCLELEVI no-co-do-ro-pv-hc 917 Poaceae Ottochloa nodosa (Kunth) Dandy BS11 OTTONODO mi-la-do-hc-ad 918 Myristicaceae Gymnacranthera forbesii (king) warb. BS11 GYMNFORB me-la-do-ct-ph 919 Verbenaceae Vitex pinnata l.f. BS11 VITEPINN no-co-do-ph 920 Asteraceae Chromolaena odorata (L.) R.M. King & H.R. BS12 CHROODOR no-pe-do-ch 921 Poaceae Imperata cylindrica (L.) Beauv. BS12 IMPECYLI me-ve-do-pv-hc-ad 922 Poaceae Pennisetum purureum Schum. BS12 PENNPURU me-ve-do-pv-hc 923 Melastomataceae Melastoma affine D.Don BS12 MELAAFFI mi-ve-do-ch 924 Myrtaceae Psidium guajava L. BS12 PSIDGUAJ no-ve-do-ch 925 Schizaeaceae Lygodium circinnatum (Burm.f.) Sw. BS12 LYGOCIRC mi-ve-do-fi-hc-li 926 Fabaceae Centrosema pubescens Benth. BS12 CENTPUBE mi-ve-do-de-cr-li 927 Fabaceae Leucaena leucacephala (Lam.) de Wit BS12 LEUCLEUC le-ve-do-ch 928 Asteraceae Blumea lacera (Burm.f.) DC. BS12 BLUMLACE mi-ve-do-hc 929 Poaceae Paspalum scorbiculatum Steud. BS12 PASPSCOR no-ve-do-pv-hc 930 Compositae Clibadium sp. BS12 CLIBSP. no-ve-do-ch 931 Poaceae Pennisetum purpureum Schum. BS13 PENNPURP me-ve-do-pv-hc 932 Asteraceae Chromolaena odorata (L.) R.M. King & R.H. BS13 CHROODOR mi-la-do-ch 933 Melastomataceae Melastoma affine D.Don BS13 MELAAFFI no-ve-do-ch

50 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 934 Myrtaceae Psidium guajava L. BS13 PSIDGUAJ no-ve-do-ch 935 Fabaceae Centrosema pubescens Benth. BS13 CENTPUBE mi-ve-do-de-cr-li 936 Fabaceae Calopogonium mucunoides Desv. BS13 CALOMUCU mi-ve-do-de-cr-li 937 Poaceae Imperata cylindrica (l.) beauv. BS13 IMPECYLI mi-ve-do-pv-hc-ad 938 Poaceae Paspalum conjugatum Berg. BS14 PASPCONJ mi-ve-do-pv-hc-ad 939 Poaceae Paspalum commersonii Lam. BS14 PASPCONJ mi-ve-do-pv-hc-ad 940 Poaceae Digitaria ciliaris (Retz.) Koel. BS14 DIGICILI na-ve-do-pv-hc-ad 941 Cyperaceae Cyperus rotundus Linn. BS14 CYPEROTU mi-ve-do-ro-pv-hc 942 Rubiaceae Hedyotis corymbosa (L.) Lam. BS14 HEDYCORY na-ve-do-hc-ad 943 Poaceae Axonopus compressus (Swartz) Beauv. BS14 AXONCOMP mi-la-do-pv-hc-ad 944 Poaceae Pennisetum purpureum Schum. BS14 PENNPURP no-ve-do-pv-hc 945 Poaceae Imperata cylindrica (L.) Beauv. BS14 IMPECYLI mi-ve-do-pv-hc-ad 946 Melastomataceae Melastoma affine D.Don BS14 MELAAFFI mi-la-do-ch 947 Rubiaceae Borreria hispida Spruce ex K.Schum. BS14 BORRHISP na-la-do-hc 948 Asteraceae Porophyllum ruderale (Jacq.) Cass. BS14 PORORUDE mi-ve-do-su-th 949 Euphorbiaceae Croton glandulosus Linn. BS14 CROTGLAN na-la-do-th 950 Fabaceae Centrosema pubescens Benth. BS14 CENTPUBE mi-ve-do-de-cr-li 951 Poaceae Paspalum scorbiculatum Steud. BS14 PASPSCOR no-ve-do-pv-hc 952 Euphorbiaceae Manihot esculenta crantz BS14 MANIESCU no-pe-do-ch 953 Poaceae Digitaria ciliaris (Retz) Koel. BS15 DIGICILI mi-ve-do-pv-hc-ad 954 Poaceae Pennisetum purpureum Schum. BS15 PENNPURP mi-ve-do-pv-hc 955 Euphorbiaceae Croton glandulosus Linn. BS15 CROTGLAN mi-co-do-hc 956 Rubiaceae Borreria hispida Spruce ex K. Schum. BS15 BORRHISP na-la-do-hc 957 Rubiaceae Hedyotes corymbosa (L.) Lam. BS15 HEDYCORY na-la-do-hc 958 Euphorbiaceae Manihot esculanta Crantz BS15 MANIESCU no-pe-do-ch 959 Poaceae Imperata cylindrica (L.) Beauv. BS15 IMPECYLI me-ve-do-pv-hc-ad 960 Asteraceae Chromolaena odorata (L.) R.M. King & H.R. BS15 CHROODOR no-pe-do-ch 961 Verbenaceae Stachytarpheta jamaicensis (L.) Vahl. BS15 STACJAMA mi-la-do-hc 962 Cyperaceae Cyperus rotundus Linn. BS15 CYPEROTU mi-ve-do-hc 963 Poaceae Axonopus compressus (Swartz Beauv. BS15 AXONCOMP mi-la-do-pv-hc-ad 964 Poaceae Sporobolus diander (Retz.) Beauv. BS15 SPORDIAN mi-ve-do-pv-hc 965 Asteraceae Ageratum conyzoides Linn. BS15 AGERCONY mi-pe-do-th 966 Melastomataceae Melastoma affine D.Don BS15 MELAAFFI mi-ve-do-ch 967 Poaceae Eleusine indica (L.) Gaertn. BS15 ELEUINDI mi-ve-do-pv-hc 968 Poaceae Echinochloa colonum (L.) Link. BS15 ECHICOLO na-ve-do-pv-hc 969 Fabaceae Centrosema pubescens Benth. BS15 CENTPUBE mi-ve-do-de-cr-li 970 Poaceae Eragrotis elongata (Willd.) Jacq. BS15 ERAGELON mi-ve-do-pv-hc 971 Poaceae Paspalum scorbiculatum steud. BS15 PASPSCOR mi-ve-do-pv-hc-ad 972 Asteraceae Chromolaena odorata (L.) R.M. King & H.R. BS16 CHROODOR no-pe-do-ch 973 Schizaeaceae Lygodium circinnatum (Burm.f.) Sw. BS16 LYGOCIRC mi-la-do-fi-hc-li 974 Euphorbiaceae Bridelia tomentosa Blume BS16 BRIDTOME mi-pe-do-ch 975 Euphorbiaceae Bridelia minutiflora Hook.f. BS16 BRIDMINU me-ve-do-pv-hc-ad 976 Verbenaceae Lantana camara Linn. BS16 LANTCAMA mi-la-do-ch 977 Fabaceae Archidendron jiringa (Jack.) I. Nielsen BS16 ARCHJIRI no-la-do-ch 978 Euphorbiaceae Antidesma ghesaembilla Gaertn. BS16 ANTIGHES mi-la-do-ch 979 Convolvulaceae Lepistemon binectariferum Kuntze BS16 LEPIBINE no-pe-do-hc-li 980 Ulmaceae Trema orientalis (L.) Blume BS16 TREMORIE no-la-do-ct-ph 981 Euphorbiaceae Aporusa dioica (Roxb.) M.A. BS16 APORDIOI me-la-do-ch 982 Menispermaceae Pericampylus glaucus Merrill BS16 PERIGLAU no-pe-do-cr-li 983 Poaceae Panicum incomtum Trin. BS16 PANIINCO me-pe-do-pv-hc-ad 984 Poaceae Panicum cordatum Buese BS16 PANICORD no-pe-do-pv-hc-ad 985 Verbenaceae Vitex pinnata Linn. BS16 VITEPINN me-la-do-ch

51 ANNEX III

Table 3. Vascular plant species and functional types listed according to site

No Family Genus Species Site Code Functional modi 986 Convolvulaceae Merremia umbellata (L.) Hallier.f. BS16 MERRUMBE no-pe-do-hc-li 987 Asteraceae Mikania cordata (Burm.f.) B.L.R. BS16 MIKACORD no-pe-do-hc-li 988 Acanthaceae Justicia sp. BS16 JUSTSPP. mi-la-do-hc 989 Fabaceae Desmodium heterocarpon (L.) DC. BS16 DESMHETE na-la-do-ch 990 Poaceae Cyrtococcum accrescens Stapf BS16 CYRTACCR mi-la-do-pv-hc 991 Poaceae Paspalum conjugatum Berg. BS16 PASPCONJ no-co-do-pv-hc-li-ad 992 Euphorbiaceae Macaranga rhizinoides (Bl.) Muell.Arg. BS16 MACARHIZ pl-la-do-ct-ph 993 Moraceae Ficus fistulosa Reinw. ex Blume BS16 FICUFIST me-la-do-ct-ph 994 Euphorbiaceae Homalanthus populneus (Grisel) Pax BS16 HOMAPOPU me-la-do-ch 995 Melastomataceae Melastoma affine D.Don BS16 MELAAFFI mi-la-do-ch 996 Malvaceae Abelmoschus moschatus (L.) Medicus BS16 ABELMOSC me-la-do-ch 997 Rubiaceae Psychotria viridiflora Reinw. ex Blume BS16 PSYCVIRI no-la-do-ch 998 Fabaceae Uraria logopodioides (L.) Desv. ex DC. BS16 URARLOGO mi-la-do-hc 999 Poaceae Cyrttococcum accrescens Stapf BS16 CYRTACCR mi-co-do-pv-hc 1000 Solanaceae Solanum torvum Swartz BS16 SOLATORV me-ve-do-ch 1001 Cyperaceae Scleria levis Retz. BS16 SCLELEVI no-co-do-pv-hc 1002 Euphorbiaceae Mallotus paniculatus (Lam.) Muell. Arg. BS16 MALLPANI me-la-do-ch 1003 Euphorbiaceae Phyllanthus niruri Linn. BS16 PHYLNIRU le-la-do-ch 1004 Thelipteris Group Cyclosorus sp. BS16 CYCLSPP. me-la-do-fi-hc-ad 1005 Leeaceae Leea indica (Burm.f.) Merr. BS16 LEEASPP. no-la-do-ch-ad 1006 Euphorbiaceae Glochidion sp. BS16 GLOCSPP. na-la-do-ch 1007 Fabaceae Mimosa invisa Martius ex Colla BS16 MIMOSPP. pi-la-do-ch-li 1008 Nephrolepis Group Nephrolepis exaltata (L.) Schott BS16 NEPHEXAL na-la-do-fi-hc-ad 1009 Sterculiaceae Commersonia bartramia Merrill BS16 COMMBART mi-la-do-ct-ph 1010 Poaceae Centotheca lappacea (L.) Desvaux BS16 CENTLAPP mi-la-do-pv-hc-ad 1011 Dioscoreaceae Dioscorea hispida Dennst. BS16 DIOSHISP no-la-do-de-cr-li 1012 Euphorbiaceae Macaranga tanarius (L.) Muell.Arg. BS16 MACATANA pl-pe-do-ch 1013 Passifloraceae Passiflora foetida Linn. BS16 PASSFOET no-la-do-hc-li 1014 Asteraceae Clibadium surinamense Linn. BS16 CLIBSURI no-pe-do-ch

52 ANNEX III

Table 4. List of bird species per benchmark sites

No. Number Species Name BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS9 BS10 BS11 BS12 BS13 BS14 BS15 BS16 Survey English

1 39 Grey Heron 0 0 0 0 0 0 0 0 * 0 * 0 X * * 0 2 53 Striated Heron 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 3 79 Oriental Honey-buzzard 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 4 90 Crested Serpent-eagle 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 5 97 Crested Goshawk 0 0 X 0 X 0 0 0 * 0 * 0 0 * * 0 6 107 Japanese Goshawk 0 0 0 0 0 0 X X * 0 * 0 0 * * 0 7 132 Black-thighed Falconet 0 0 0 0 0 X X 0 * 0 * 0 0 * * 0 8 144 Wandering Whistling-duck 0 0 0 0 0 0 0 0 * 0 * X 0 * * 0 9 190 Red Junglefowl 0 0 0 0 0 0 0 0 * 0 * X X * * 0 10 199 Barred Button-quail 0 0 0 0 0 X 0 0 * 0 * X X * * X 11 225 White-breasted Waterhen 0 0 0 0 0 0 0 0 * 0 * 0 X * * 0 12 244 Little Ringed Plover 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 13 268 Common Sandpiper 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 14 275 Pintail Snipe 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 15 295 Oriental Pratincole 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 16 324 Thick-billed Green Pigeon 0 0 0 0 0 0 0 0 * 0 * X X * * 0 17 331 Pink-necked Gree Pigeon 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 18 337 (Jambu) Fruit-dove 0 0 0 0 0 0 0 0 * 0 * 0 0 * * X 19 360 Green Imperial Pigeon 0 0 0 0 0 0 0 0 * X * 0 0 * * 0 20 395 Spotted Dove 0 0 0 0 0 0 0 0 * 0 * X X * * X 21 397 Zebra Dove 0 0 0 0 0 0 0 0 * 0 * X 0 * * 0 22 400 Emerald Dove 0 0 X X X 0 0 X * X * 0 0 * * X 23 472 Long-tailed Parakeet 0 0 0 0 0 0 0 0 * 0 * X X * * 0 24 481 Blue-rumped Parrot X X X X X X 0 X * 0 * 0 0 * * 0 25 482 Blue-crowned Hanging-parrot X X X X X X 0 X * 0 * 0 0 * * 0 26 495 Indian Cuckoo 0 0 0 0 0 0 X 0 * 0 * 0 0 * * X 27 497 Oriental Cuckoo 0 0 0 X X 0 0 0 * 0 * 0 0 * * 0 28 499 Banded Bay Cuckoo X 0 0 X 0 0 0 0 * X * 0 0 * * 0 29 500 Plaintive Cuckoo 0 0 0 0 0 X X 0 * 0 * X X * * X 30 519 Drongo Cuckoo 0 0 0 0 0 0 X 0 * X * 0 0 * * X 31 526 Chestnut-bellied Malkoha 0 0 0 0 0 0 0 0 * X * 0 0 * * 0 32 529 Red-billed Malkhoa 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 33 531 Chestnut-breasted Malkoha 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 34 540 Greater Coucal 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 35 542 Lesser Coucal 0 0 0 0 0 X X X * X * X X * * X 36 564 Collared Scops owl 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 37 568 Buffy Fish-owl 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 38 576 Brown Boobook 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 39 602 Malaysian-eared Nightjar 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 40 607 Savanna Nightjar 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 41 614 Edible-nest Swiftlet 0 0 X X 0 X 0 0 * 0 * 0 0 * * 0 42 622 White-throated Needletail 0 0 0 0 0 X 0 0 * 0 * 0 0 * * 0 43 626 Silver-rumped Swift 0 0 0 0 0 X 0 0 * 0 * 0 0 * * 0 44 630 Asian Palm-swift 0 0 0 0 0 X 0 0 * 0 * 0 0 * * 0 45 631 Grey-rumped Tree-swift 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 46 633 Whiskered Tree-swift 0 X X X X X 0 0 * 0 * 0 0 * * 0 47 635 Red-naped Trogon X X 0 0 X 0 0 0 * 0 * 0 0 * * 0 48 637 Orange-breasted Trogon 0 0 0 X 0 0 0 0 * 0 * 0 0 * * 0 49 648 Oriental Dwarf Kingfisher X X 0 0 X 0 0 0 * 0 * 0 0 * * 0 50 650 Stork-billed Kingfisher 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 51 660 White-breasted Kingfisher 0 0 0 0 0 0 X X * 0 * X X * * 0 52 672 Collared Kingfisher 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 53 687 Blue-tailed Bee-eater 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 54 689 Blue-throated Bee-eater 0 X 0 X X X X X * X * X X * * X 55 690 Red-bearded Bee-eater 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 56 693 Common Dollarbird 0 0 0 0 0 0 0 0 * 0 * X 0 * * X 57 701 Wreathed Hornbill 0 0 0 0 0 0 0 0 * 0 * X 0 * * 0 58 705 Black Hornbill 0 0 0 X X 0 X 0 * X * 0 0 * * 0 59 706 Asian Pied Hornbill 0 0 X 0 0 0 0 0 * X * 0 0 * * 0

53 ANNEX III

Table 4. List of bird species per benchmark sites

No. Number Species Name BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS9 BS10 BS11 BS12 BS13 BS14 BS15 BS16 Survey English 60 707 Rhinoceros Hornbill X 0 0 X 0 0 0 0 * X * 0 0 * * 0 61 709 Helmeted Hornbill X X X X X 0 0 0 * 0 * 0 0 * * 0 62 714 Red-crowned Barbet X X X X X 0 0 0 * 0 * 0 0 * * 0 63 721 Blue-eared Barbet 0 0 X X 0 0 0 0 * X * 0 0 * * X 64 724 Brown Barbet 0 0 0 0 0 0 0 0 * X * 0 0 * * 0 65 727 Rufous Piculet 0 0 0 0 0 0 0 0 * X * 0 0 * * 0 66 734 Crimson-winged Yellownape 0 0 X X 0 0 0 X * X * 0 0 * * 0 67 737 Olive-backed Woodpecker X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 68 739 Buff-necked Woodpecker X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 69 742 White-bellied Woodpecker X X X X 0 0 0 0 * 0 * 0 0 * * 0 70 747 Grey-and-buff Woodpecker 0 0 X 0 X 0 0 0 * 0 * 0 0 * * 0 71 748 Maroon Woodpecker 0 0 0 X 0 0 0 0 * 0 * 0 0 * * 0 72 750 Greater Goldenback 0 0 0 0 0 0 0 0 * 0 * 0 X * * 0 73 751 Dusky Broadbill 0 0 X 0 0 0 0 0 * 0 * 0 0 * * 0 74 754 Black-and-yellow Broadbill 0 0 X X X 0 X X * X * 0 0 * * 0 75 776 Barn Swallow 0 0 0 0 X X X 0 * 0 * X X * * X 76 799 Bar-bellied Cuckoo-shrike X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 77 829 Fiery Minivet 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 78 830 Unidentified minivet 0 X X 0 0 0 0 0 * 0 * 0 0 * * 0 79 833 Scarlet Minivet 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 80 835 Black-winged Hemipus 0 X X 0 0 0 0 0 * 0 * 0 0 * * 0 81 836 Large Wood-shrike 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 82 840 unidentified bulbul X X 0 X 0 0 0 0 * 0 * 0 0 * * 0 83 841 Black-headed Bulbul 0 0 0 X X 0 0 X * X * 0 0 * * 0 84 842 Black-crested Bulbul 0 0 0 0 0 0 0 X * X * 0 0 * * 0 85 845 Sooty-headed Bulbul 0 0 0 0 0 0 0 0 * 0 * X X * * 0 86 849 Yellow-vented Bulbul 0 0 0 0 0 X X 0 * 0 * X X * * X 87 851 Cream-vented Bulbul 0 0 0 X 0 X 0 0 * X * 0 0 * * 0 88 852 Red-eyed Bulbul 0 0 X X X X X X * X * 0 0 * * X 89 857 Yellow-bellied Bulbul X X X 0 X 0 0 0 * 0 * 0 0 * * 0 90 860 Hairy-backed Bulbul 0 0 X 0 0 0 0 0 * 0 * 0 0 * * 0 91 865 Common Iora 0 0 0 0 0 0 0 0 * 0 * 0 0 * * X 92 866 Green Iora 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 93 867 Greater Green Leafbird X X X 0 0 0 0 0 * 0 * 0 0 * * 0 94 868 Lesser Green Leafbird 0 0 0 X X 0 0 0 * 0 * 0 0 * * 0 95 869 Blue-winged Leafbird 0 0 X 0 0 0 0 0 * 0 * 0 0 * * 0 96 872 Asian Fairy Bluebird 0 X 0 0 0 X 0 0 * 0 * 0 0 * * 0 97 873 Tiger Shrike 0 0 0 0 0 0 0 0 * 0 * 0 0 * * X 98 875 Long-tailed Shrike 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 99 882 Oriental Magpie-robin 0 0 0 0 0 X X 0 * 0 * X X * * X 100 883 White-rumped Shama 0 X 0 X 0 0 0 0 * 0 * 0 0 * * 0 101 915 Eye-browed Thrush 0 0 0 0 0 X X 0 * 0 * 0 0 * * 0 102 922 Rail-babbler 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 103 926 Black-capped Babbler 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 104 929 Short-tailed Babbler 0 0 X 0 0 0 0 0 * X * 0 0 * * 0 105 931 Ferruginous Babbler 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 106 932 Horsfield's Babbler X X 0 0 0 0 0 0 * 0 * 0 0 * * 0 107 934 Abbott's Babbler 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 108 937 Moustached Babbler X X 0 0 X 0 0 0 * 0 * 0 0 * * 0 109 939 Scaly-crowned Babbler 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 110 940 Rufous-crowned Babbler X 0 0 0 0 0 0 0 * 0 * 0 0 * * X 111 942 Chestnut-backed Scimitar- 0 0 0 0 X 0 0 X * 0 * 0 0 * * 0 babbler 112 947 Striped Wren-babbler 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 113 955 Unidentified Stachyris X X 0 0 0 0 X 0 * 0 * 0 0 * * 0 114 961 Chestnut-rumped Babbler 0 0 0 X 0 0 0 0 * 0 * 0 0 * * X 115 963 Black-throated Babbler 0 0 X 0 0 0 0 0 * 0 * 0 0 * * 0 116 965 Chestnut-winged Babbler 0 0 X X X 0 0 0 * X * 0 0 * * 0 117 968 Striped Tit-babbler 0 0 0 X 0 X X 0 * X * X X * * X 118 979 Brown Fulvetta 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0

54 ANNEX III

Table 4. List of bird species per benchmark sites

No. Number Species Name BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS9 BS10 BS11 BS12 BS13 BS14 BS15 BS16 Survey English

119 990 Sunda Bush-warbler 0 0 0 0 0 0 0 0 * X * 0 0 * * 0 120 1005 Zitting Cisticola 0 0 0 0 0 0 0 0 * 0 * X X * * 0 121 1006 Golden-headed Cisticola 0 0 0 0 0 0 0 0 * 0 * X 0 * * X 122 1009 Bar-winged Prinia 0 0 0 0 0 X X 0 * X * X X * * X 123 1010 Yellow-bellied Prinia 0 0 0 0 0 X 0 X * 0 * X X * * X 124 1014 Dark-necked Tailorbird 0 X X X X X X X * X * X X * * X 125 1015 Rufous-tailed Tailorbird 0 X X X X X X 0 * 0 * 0 0 * * 0 126 1016 Ashy Tailorbird X X X X X X X 0 * 0 * X X * * X 127 1037 Asian Brown Flycatcher 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 128 1068 Tickell's Blue Flycatcher X X X X X 0 0 0 * 0 * 0 0 * * 0 129 1070 Unidentified flycatcher 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 130 1071 Grey-headed Flycatcher X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 131 1099 Rufous-winged Philentoma 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 132 1101 Black-naped Monarch 0 X X 0 X 0 0 0 * X * 0 0 * * 0 133 1103 Asian Paradise-flycatcher 0 X 0 0 X 0 0 0 * 0 * 0 0 * * 0 134 1141 Spotted Fantail 0 0 0 X X 0 0 0 * 0 * 0 0 * * 0 135 1228 Crimson-breasted 0 0 0 0 0 0 0 X * X * 0 0 * * 0 Flowerpecker 136 1230 Unidentified flowerpecker 0 0 X X X 0 0 0 * 0 * 0 0 * * 0 137 1236 Orange-bellied Flowerpecker X X X X X 0 0 0 * X * 0 0 * * X 138 1254 Brown-throated Sunbird X 0 0 X X 0 X 0 * X * 0 0 * * X 139 1253 Plain Sunbird 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 140 1257 Purple-naped Sunbird 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0 141 1261 Olive-backed Sunbird 0 0 0 0 0 0 X X * 0 * 0 0 * * 0 142 1266 Crimson Sunbird 0 0 X X X 0 0 0 * 0 * 0 0 * * 0 143 1269 Little Spiderhunter X 0 X X X 0 0 0 * 0 * 0 0 * * 0 144 1274 Grey-breasted Spiderhunter X X X 0 X 0 0 0 * 0 * 0 0 * * 0 145 1275 Oriental White-eye 0 0 X 0 X 0 0 0 * 0 * 0 0 * * 0 146 1398 White-bellied Munia 0 0 0 0 0 0 0 0 * 0 * 0 0 * * X 147 1397 Scaly-breasted Munia 0 0 0 0 0 X 0 0 * 0 * X X * * 0 148 1403 White-headed Munia 0 0 0 0 0 0 0 0 * 0 * X X * * X 149 1415 Tree Sparrow 0 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 150 1424 Asian Glossy Starling X X X X X 0 0 0 * 0 * 0 0 * * 0 151 1441 Hill Myna 0 X X 0 X X X X * 0 * 0 X * * X 152 1451 Dark-throated Oriole 0 0 0 X X 0 0 0 * X * 0 0 * * 0 153 1462 Bronzed Drongo 0 0 X 0 X 0 0 0 * 0 * 0 0 * * 0 154 1469 Greater Racquet-tailed X X X X X 0 X 0 * X * 0 0 * * X Drongo 155 1520 Black Magpie 0 0 0 0 0 0 0 X * X * 0 0 * * 0 156 1526 Slender-billed Crow 0 X 0 0 0 X X X * X * 0 0 * * 0 157 1600 Species A X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 158 1601 Species B X 0 0 0 0 0 0 0 * 0 * 0 0 * * 0 159 1602 Species C 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 160 1603 Specices D 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 161 1604 Species E 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 162 1605 Species F 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 163 1606 Species G 0 X 0 0 0 0 0 0 * 0 * 0 0 * * 0 164 1607 Species H 0 0 X 0 0 0 0 0 * 0 * 0 0 * * 0 165 1608 Species I 0 0 X X 0 0 0 0 * 0 * 0 0 * * 0 166 1609 Species J 0 0 0 0 X 0 0 0 * 0 * 0 0 * * 0

30 41 42 41 54 28 26 20 * 33 * 26 25 * * 30

ANNEX III

Table 5. List of large mammal species per benchmark sites

No. Species Code BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS9 BS10 BS11 BS12 BS13 BS14 BS15 BS16 1 Macaca fascicularis MACAFASC 1 1 1 0 1 0 0 0 * 0 * 0 * 0 * * 2 Presbytis melalophos PRESMELA 1 1 1 1 1 1 0 1 * 1 * 0 * 0 * * 3 Callosciurus prevostii CALLPREV 1 0 0 1 0 1 0 1 * 1 * 0 * 0 * * 4 Pteropus vampirus PTERVAMP 1 0 0 0 0 0 0 0 * 0 * 0 * 0 * * 5 Helarctos malayanus HELAMALA 1 1 0 0 0 1 0 0 * 0 * 0 * 0 * * 6 Sus barbatus SUSBARBA 1 1 0 1 0 1 1 1 * 1 * 0 * 0 * * 7 Hylobates lar agilis HYLOLARA 1 0 1 1 1 1 1 0 * 0 * 0 * 0 * * 8 Hemigalus derbyanus HEMIDERB 1 0 0 0 0 0 0 0 * 0 * 0 * 0 * * 9 Trachyphitecus cristatus TRACCRIS 0 0 1 0 0 0 0 0 * 1 * 0 * 0 * * 10 Tragulus javanicus TRAGJAVA 0 0 0 1 0 0 0 0 * 0 * 0 * 0 * * 11 Petinomys genigarbis PETIGENI 0 0 0 1 0 0 0 0 * 0 * 0 * 0 * * 12 Ratufa affinis RATUAFFI 0 0 0 0 0 1 0 0 * 0 * 0 * 0 * * 13 Felis bengalensis FELIBENG 0 0 0 0 0 0 0 1 * 0 * 0 * 0 * * 14 Sundasciurus hippurus SUNDHIPP 0 0 0 0 0 0 0 0 * 1 * 0 * 0 * * 15 Callosciurus notatus CALLNOTA 0 0 0 0 0 0 0 0 * 1 * 0 * 0 * * 16 Cervus unicolor CERVUNIC 0 0 0 0 0 0 1 0 * 1 * 0 * 1 * * 17 Sus scrofa SUSSCROF 0 0 0 0 0 0 0 0 * 0 * 1 * 1 * * 18 Muntiacus muntjak MUNTMUNT 0 0 0 0 1 0 0 0 * 0 * 0 * 0 * * 19 Tupaia tana TUPATANA 0 0 0 0 0 0 0 0 * 1 * 0 * 0 * * 20 Tupaia glis TUPAGLIS 0 0 0 0 0 0 0 0 * 1 * 0 * 0 * * 21 Sundasciurus lowii SUNDLOWI 0 0 0 0 0 0 0 0 * 1 * 0 * 0 * *

ANNEX III

Table 6. List of small mammal species per benchmark sites

No. Species Code BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 BS9 BS10 BS11 BS12 BS13 BS14 BS15 BS16 1 Balionycteris maculata BALIMACU 0 0 0 1 1 0 0 0 0 0 0 0 0 * * * 2 Cynopterus brachyotis CYNOBRAC 0 0 0 0 0 0 0 1 1 1 1 0 0 * * * 3 Maxomys rajah MAXOWHIT 1 1 0 1 1 0 0 0 0 0 0 0 0 * * * 4 Maxomys whiteheadi MAXOWHIT 0 0 1 1 1 1 0 1 1 1 1 0 0 * * * 5 Pipistrellus javanicus PIPIJAVA 0 0 0 0 0 0 1 0 0 0 0 0 0 * * * 6 Rhinolophus lepidus RHINLEPI 0 0 0 1 1 0 0 0 0 0 0 0 0 * * * 7 Rattus rattus RATTRATT 0 0 0 0 0 1 0 0 0 0 0 0 0 * * * 8 Rattus exulans RATTEXUL 0 0 0 0 0 1 1 1 1 0 0 1 1 * * * 9 Rattus tiomanicus RATTTIOM 0 0 0 0 0 0 0 0 0 0 0 1 1 * * * 10 Rousettus amplexicaudatus ROUSAMPL 0 0 0 0 0 1 0 0 0 0 0 0 0 * * *

57 ANNEX III

Table 7. Site data for large mammals per benchmark sites

NO. SITE LAND TYPE LOCALITY DATE CONTACT LOCAL NAME COMMON NAME SPECIES DIRECT/ NUMBERS DISTANCE DIRECTION LOCATION FUNCTION NO. TIME INDIRECT OF INDIV. (M) (N-E)

1 BS-1 Intact rain Pasir Mayang Nov. 19 1 16.30 Monyet ekor panjang Long-tailed macaques Macaca fascicularis Direct 7 150 130 forest 2 16.35 Simpai Banded langur Presbytis melalophos Direct 4 100 120 3 16.40 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 100 270 4 16.50 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 inside 5 17.15 Kalong Flying fox Pteropus vampirus Direct 1 inside 6 - Beruang madu Sun bear Helarctos malayanus Indirect - TS 1 70 110 7 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 50 10

Nov. 20 1 6.30 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 100 170 2 6.40 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 150 220 3 10.56 Owa Agile gibbon Hylobates lar agilis Direct 3 150 60 4 11.05 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 200 65

Nov. 25 1 20.15 Musang Banded palm cibet Hemigalus derbyanus Direct 1 200 50

2 BS-2 Intact rain Pasir Mayang Nov. 20 1 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 20 150 forest 2 16.40 Beruang madu Sun bear Helarctos malayanus Indirect - TS 1 150 170

Nov. 21 1 6.35 Simpai Banded langur Presbytis melalophos Indirect - CS 1 100 240 2 6.55 Monyet ekor panjang Long-tailed macaques Macaca fascicularis Direct 5 100 175

3 BS-3 Secondary Pasir Mayang Nov. 21 1 16.35 Simpai Banded langur Presbytis melalophos Direct 7 50 35 forest 2 16.45 Monyet ekor panjang Long-tailed macaques Macaca fascicularis Direct 8 75 320 3 17.10 Lutung SIlvered langur Trachyphitecus cristatus Direct 3 50 340

Nov. 22 1 6.30 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 250 30 2 6.34 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 400 335 3 6.43 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 200 200 Primary 4 6.55 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 100 120 forest 5 7.15 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 100 165 6 7.18 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 150 225 Primary 7 7.30 Simpai Banded langur Presbytis melalophos Indirect - CS 1 50 280 forest 8 7.45 Monyet ekor panjang Long-tailed macaques Macaca fascicularis Direct 3 40 190 9 8.00 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 2 30 265 4 BS-4 LOA 1983 -1 Pasir Mayang Nov. 22 1 17.15 Kancil Lesser mouse-deer Tragulus javanicus Direct 1 20 145 2 17.35 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 40 130

58 ANNEX III

Table 7. Site data for large mammals per benchmark sites

NO. SITE LAND TYPE LOCALITY DATE CONTACT LOCAL NAME COMMON NAME SPECIES DIRECT/ NUMBERS DISTANCE DIRECTION LOCATION FUNCTION NO. TIME INDIRECT OF INDIV. (M) (N-E)

3 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 15 130 Nov. 23 1 6.10 Bajing terbang Whiskered flying squirrel Petinomys genigarbis Direct 1 200 280 2 6.20 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 7 190 260 3 6.22 Owa Agile gibbon Hylobates lar agilis Direct 1 200 270 4 6.40 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 15 310 5 7.28 Owa Agile gibbon Hylobates lar agilis Direct 4 100 150 6 7.45 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 200 60 7 8.00 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 inside 8 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 200 250 5 BS-6 P. falcataria Pasir Mayang Nov. 24 1 6.30 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 2 150 120 LOA 1993/1994 - 1 2 6.35 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 400 350 LOA 3 6.40 Simpai Banded langur Presbytis melalophos Indirect - CS 1 200 300 LOA 4 7.10 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 250 210 LOA 5 7.12 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 400 250 LOA 6 7.15 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 300 280 LOA 7 7.40 Bajing Giant squirrel Ratufa affinis Direct 1 10 195 8 8.00 Beruang madu Sun bear Helarctos malayanus Indirect - FP 1 15 210 9 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 20 120

6 BS-8 Rubber Pasir Mayang Nov. 24 1 16.34 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 5 50 290 plantation 2 17.03 Babi hutan Bearded pig Sus barbatus Direct 1 40 310 3 17.18 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 30 210

Nov. 25 1 6.30 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 50 170 2 7.16 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 3 100 355 LOA 3 10.50 Kucing hutan Leopard cat Felis bengalensis Direct 1 100 80

7 BS-10 Jungle rubber Pancuran Nov. 25 1 16.30 Bajing Horse-tailed squirrel Sundasciurus hippurus Direct 1 150 230 Gading 2 16.35 Bajing Plantain squirrel Callosciurus notatus Direct 1 15 240 3 16.35 Lutung Silvered langur Trachypithecus cristatus Direct 2 40 260 4 16.42 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 25 250 5 16.45 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 15 265 6 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 40 260 7 - Sambar Sambar deer Cervus unicolor Indirect - SS 1 70 300

59 ANNEX III

Table 7. Site data for large mammals per benchmark sites

FUNCTION NO. SITE LAND TYPE LOCALITY DATE CONTACT LOCAL NAME COMMON NAME SPECIES DIRECT/ NUMBERS DISTANCE DIRECTION LOCATION NO. TIME INDIRECT OF INDIV. (M) (N-E) Nov. 26 1 6.30 Tupai tanah Large treeshrew Tupaia tana Direct 1 10 260 2 6.35 Bajing Prevost's squirrel Callosciurus prevostii Direct 1 10 230 3 6.41 Tupai Common treeshrew Tupaia glis Direct 1 15 280 4 6.45 Bajing Low's squirrel Sundasciurus lowii Direct 1 10 120 5 7.35 Simpai Banded langur Presbytis melalophos Indirect - CS 1 100 320

8 BS-14 Cassava Kuamang Nov. 26 1 - Babi hutan Domestic pig Sus scrofa Indirect - FP 1 inside Kuning 2 - Sambar Sambar deer Cervus unicolor Indirect - FP 1 15 170

Nov. 27 - -

9 BS-12 Imperata Kuamang Nov. 26 1 - Babi hutan Domestic pig Sus scrofa Indirect - FP 1 inside Kuning

Nov. 27 - -

10 BS-7 P. falcataria Pasir Mayang Nov. 27 1 - Sambar deer Sambar deer Cervus unicolor Indirect - FP 1 inside 1993/1994 - 2 2 - Babi hutan Bearded pig Sus barbatus Indirect - FP 1 inside

Nov. 28 1 6.43 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 750 30 LOA 2 6.52 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 800 70 LOA

11 BS-5 LOA 1983 - 2 Pasir Mayang Nov. 28 1 16.32 Simpai Banded langur Presbytis melalophos Indirect - CS 1 150 140 2 17.15 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 3 inside 3 17.25 Monyet ekor panjang Long-tailed macaques Macaca fascicularis Indirect - CS 6 100 125 4 17.43 Simpai belang putih Banded langur (BW) Presbytis melalophos Direct 1 inside

Nov. 29 1 6.30 Owa Agile gibbon Hylobates lar agilis Direct 1 inside 2 6.31 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 400 30 3 7.14 Simpai Banded langur Presbytis melalophos Indirect - CS 1 200 140 4 7.19 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 200 50 5 7.22 Kijang Barking deer Muntiacus muntjak Direct 1 150 350 6 7.32 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 200 280 7 7.53 Owa Agile gibbon Hylobates lar agilis Indirect - CS 1 250 115

ANNEX III

Table 8. Average counts of canopy insects per benchmark sites

No. Site Ants Beet Spid Rest Tt Arth Isop Neur Hem Hym Thy Psoc Acar Orth Dip Lep Coll Blatt Total

1 BS1 41.80 5.30 1.90 80.5 129.4 0.10 0.10 2.00 2.90 1.90 3.70 1.30 0.80 3.40 0.60 7.50 0.50 283.70 2 BS2 5.40 3.50 2.10 104.9 116 56.80 0.20 2.50 1.30 0.80 3.80 1.00 0.60 2.60 0.70 4.60 0.40 307.20 3 BS3 36.00 6.00 4.90 31.2 78 0.70 0.20 8.00 3.70 2.70 4.60 2.40 1.50 3.40 1.50 3.70 1.80 190.30 4 BS4 8.00 8.20 5.60 44.7 66.6 0.00 0.10 6.10 3.30 8.50 7.10 2.40 0.60 4.50 0.80 5.40 0.20 172.10 5 BS5 16.60 5.30 4.80 111.8 161.5 168.10 0.20 7.90 3.90 3.10 5.80 0.60 1.50 7.50 1.50 4.60 0.40 505.10 6 BS6 8.20 3.30 2.50 8.5 22.5 0.00 0.00 2.30 1.20 0.60 3.90 0.20 0.00 0.80 0.70 0.00 0.00 54.70 7 BS7 21.00 2.80 4.40 23.9 53.2 0.00 0.00 10.80 3.10 2.80 0.60 0.40 0.00 6.20 5.80 0.00 0.00 135.00 8 BS8 3.50 3.80 2.80 23.3 35.8 0.10 0.30 2.10 2.80 3.60 2.30 0.20 0.30 14.10 0.40 0.10 0.10 95.60 9 BS9 0.60 10.80 4.10 25.7 41.2 0.00 0.20 1.00 3.20 5.40 4.90 0.20 0.10 9.90 0.40 0.60 0.00 108.30 10 BS10 49.20 13.20 4.10 46.1 112.5 0.00 0.00 6.20 4.40 16.60 2.50 0.20 1.30 7.30 2.40 1.20 0.40 267.60 11 BS11 158.90 29.20 13.30 91.8 293 1.40 0.00 11.70 9.10 11.60 9.00 0.80 2.10 10.80 2.20 2.30 2.20 649.40 12 BS12 * * * * * * * * * * * * * * * * * 13 BS13 * * * * * * * * * * * * * * * * * 14 BS14 * * * * * * * * * * * * * * * * * 15 BS15 * * * * * * * * * * * * * * * * * 16 BS16 * * * * * * * * * * * * * * * * * 17 Total 349.20 91.40 50.50 592.4 1109.7 227.20 1.30 60.60 38.90 57.60 48.20 9.70 8.80 70.50 17.00 30.00 6.00 2769.00

Beet: Beetles; Spid: Spiders; Rest= unident. Tt Arth: Total Arthropods; Isop: Isoptera; Neur: Neuroptera; Hem: Hemiptera; Hym: Hymenoptera; Thy: Thysanoptera: Psoc: Psocoptera: Acar: Acari: Orth: Orthoptera; Dip: Diptera; Lep: Lepidoptera; Coll: Collembola; Blatt: Blattodea.

61 ANNEX III

Table 9. Beetle trophic groups, example from site 1 (BS01)

SITE HABITAT TRAY-NO FAMILY FAM-NO SPEC-NO TROPHIC LEVEL-PRIMARY TROPHIC LEVEL-SECONDARY

BS01 INTACT RAINFOREST 35 SCARABAEIDAE 8 8.01 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 28 SCARABAEIDAE 8 8.02 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 47 SCARABAEIDAE 8 8.03 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 SCARABAEIDAE 8 8.05 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ELATERIDAE 12 12.15 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 50 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 ELATERIDAE 12 12.16 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 33 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 34 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 33 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 31 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 37 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 45 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ELATERIDAE 12 12.17 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 46 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 49 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 39 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 41 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 29 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 29 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ELATERIDAE 12 12.21 phytophages:chewers scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 MORDELLIDAE 28 28.04 phytophages:chewers BS01 INTACT RAINFOREST 33 MORDELLIDAE 28 28.12 phytophages:chewers BS01 INTACT RAINFOREST 41 MORDELLIDAE 28 28.12 phytophages:chewers BS01 INTACT RAINFOREST 46 MORDELLIDAE 28 28.16 phytophages:chewers BS01 INTACT RAINFOREST 46 CHRYSOMELIDAE 33 33.08 phytophages:chewers BS01 INTACT RAINFOREST 36 CHRYSOMELIDAE 33 33.13 phytophages:chewers BS01 INTACT RAINFOREST 47 CHRYSOMELIDAE 33 33.34 phytophages:chewers BS01 INTACT RAINFOREST 33 CHRYSOMELIDAE 33 33.36 phytophages:chewers BS01 INTACT RAINFOREST 28 CHRYSOMELIDAE 33 33.48 phytophages:chewers BS01 INTACT RAINFOREST 30 BRENTIDAE 36 36.01 phytophages:chewers BS01 INTACT RAINFOREST 27 CERAMBYCIDAE 32 32.15 phytophages:chewers BS01 INTACT RAINFOREST 35 CURCULIONIDAE 37 37.2 phytophages:chewers BS01 INTACT RAINFOREST 44 CURCULIONIDAE 37 37.21 phytophages:chewers BS01 INTACT RAINFOREST 43 CURCULIONIDAE 37 37.31 phytophages:chewers BS01 INTACT RAINFOREST 34 CURCULIONIDAE 37 37.34 phytophages:chewers BS01 INTACT RAINFOREST 47 CURCULIONIDAE 37 37.48 phytophages:chewers BS01 INTACT RAINFOREST 49 CURCULIONIDAE 37 37.49 phytophages:chewers BS01 INTACT RAINFOREST 41 CURCULIONIDAE 37 37.52 phytophages:chewers BS01 INTACT RAINFOREST 44 CURCULIONIDAE 37 37.54 phytophages:chewers BS01 INTACT RAINFOREST CURCULIONIDAE 37 37.55 phytophages:chewers BS01 INTACT RAINFOREST 46 STAPHYLINIDAE 6 6.15 predators scavengers,dead wood,fungal feeders

62 ANNEX III

Table 9. Beetle trophic groups, example from site 1 (BS01)

SITE HABITAT TRAY-NO FAMILY FAM-NO SPEC-NO TROPHIC LEVEL-PRIMARY TROPHIC LEVEL-SECONDARY

BS01 INTACT RAINFOREST 50 STAPHYLINIDAE 6 6.16 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 46 STAPHYLINIDAE 6 6.2 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 STAPHYLINIDAE 6 6.22 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 36 STAPHYLINIDAE 6 6.25 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 STAPHYLINIDAE 6 6.25 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 STAPHYLINIDAE 6 6.25 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 STAPHYLINIDAE 6 6.25 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 37 STAPHYLINIDAE 6 6.26 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 STAPHYLINIDAE 6 6.27 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 36 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 27 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 34 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 46 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 31 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 47 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 47 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 47 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 42 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 49 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 36 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 39 STAPHYLINIDAE 6 6.29 predators scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 27 CARABIDAE 1 1.03 predators BS01 INTACT RAINFOREST 36 CLERIDAE 17 17.10 predators BS01 INTACT RAINFOREST 28 COCCINELLIDAE 24 24.09 predators BS01 INTACT RAINFOREST 46 COCCINELLIDAE 24 24.1 predators BS01 INTACT RAINFOREST 45 COCCINELLIDAE 24 24.13 predators BS01 INTACT RAINFOREST 43 COCCINELLIDAE 24 24.16 predators BS01 INTACT RAINFOREST 42 COCCINELLIDAE 24 24.18 predators BS01 INTACT RAINFOREST 42 COCCINELLIDAE 24 24.29 predators BS01 INTACT RAINFOREST 47 COCCINELLIDAE 24 24.31 predators BS01 INTACT RAINFOREST 45 COCCINELLIDAE 24 24.31 predators BS01 INTACT RAINFOREST 43 COCCINELLIDAE 24 24.31 predators BS01 INTACT RAINFOREST 44 TENEBRIONIDAE 29 29.03 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 47 TENEBRIONIDAE 29 29.04 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 28 TENEBRIONIDAE 29 29.1 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 46 TENEBRIONIDAE 29 29.22 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 46 TENEBRIONIDAE 29 29.24 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 28 TENEBRIONIDAE 29 29.25 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 37 TENEBRIONIDAE 29 29.33 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 38 TENEBRIONIDAE 29 29.4 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 35 TENEBRIONIDAE 29 29.61 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 43 ANTHRIBIDAE 34 34.04 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 28 ANTHRIBIDAE 34 34.06 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 49 ANTHRIBIDAE 34 34.12 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 23 ANTHRIBIDAE 34 34.21 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 26 ANTHRIBIDAE 34 34.26 scavengers,dead wood,fungal feeders phytophages:chewers BS01 INTACT RAINFOREST 49 NITIDULIDAE 19 19.01 scavengers,dead wood,fungal feeders predators BS01 INTACT RAINFOREST 45 THROSCIDAE 11 11.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders

63 ANNEX III

Table 9. Beetle trophic groups, example from site 1 (BS01)

SITE HABITAT TRAY-NO FAMILY FAM-NO SPEC-NO TROPHIC LEVEL-PRIMARY TROPHIC LEVEL-SECONDARY

BS01 SECOND. RAINFOREST 39 LATRIDIIDAE 26 26.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 49 ANOBIIDAE 16 16.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 50 ANOBIIDAE 16 16.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 37 ANOBIIDAE 16 16.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 33 ANOBIIDAE 16 16.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 46 ANOBIIDAE 16 16.07 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 CORYLOPHIDAE 25 25.12 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 41 CORYLOPHIDAE 25 25.3 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ANTHICIDAE 30 30.05 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 ADERIDAE 31 31.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 44 ADERIDAE 31 31.01 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 49 ADERIDAE 31 31.02 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ADERIDAE 31 31.06 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 35 ADERIDAE 31 31.06 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 30 ADERIDAE 31 31.11 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 29 ADERIDAE 31 31.14 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 43 PHALACRIDAE 21 21.07 scavengers,dead wood,fungal feeders BS01 INTACT RAINFOREST 42 PHALACRIDAE 21 21.08 scavengers,dead wood,fungal feeders

64 ANNEX III

Table 10. Lepidoptera family richness per land use type

No. Family BS1 BS3 BS4 BS6 BS10 BS12 BS14

1 Arctuidae 1 1 1 1 1 * 1 2 Cossidae * 1 1 1 * * * 3 Danaidae 1 1 1 * 1 1 * 4 Geometridae 1 1 1 1 1 1 * 5 Laciocompidae 1 * 1 1 * * * 6 Lycaenidae * * 1 * * * * 7 Lymantridae 1 1 * * 1 * * 8 Noctuidae 1 1 1 1 1 * 1 9 Notodontidae 1 1 * * * * * 10 Nymphalidae 1 * 1 1 1 1 * 11 Papilionidae 1 1 1 1 1 * * 12 Pteridae 1 1 1 1 1 * 1 13 Pyralidae 1 1 1 * 1 * * 14 Saturnidae 1 1 1 1 1 * * 15 Satyridae * 1 * * 1 1 1 16 Riodinidae 1 * * * * * * 17 Hispridae * 1 1 * 1 * * 18 Sphingdae 1 1 1 * * * 1 Total famili 14 14 14 9 12 4 5

ANNEX III

Table 11. Termite species list per benchmark site

No. Family Species BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 S9 BS10 S11 S12 BS13 S14 BS15 BS16 Total

1 Rhinotermitidae 1 Coptotermes sp1 1 * 1 * * 1 * 1 * 1 * 0 * 0 * * 5 2 Coptotermes sp2 0 * 0 * * 1 * 0 * 0 * 0 * 0 * * 1 3 Coptotermes sp3 0 * 0 * * 1 * 0 * 0 * 0 * 0 * * 1 4 Parrhinotermes sp1 0 * 1 * * 0 * 0 * 1 * 0 * 0 * * 2 5 Schedorhinotermes sp1 1 * 0 * * 1 * 1 * 0 * 0 * 0 * * 3 6 Schedorhinotermes sp2 1 * 1 * * 1 * 1 * 1 * 0 * 0 * * 5 7 Schedorhinotermes sp3 0 * 1 * * 0 * 0 * 1 * 0 * 0 * * 2 8 Heterotermes sp1 1 * 0 * * 0 * 0 * 0 * 0 * 0 * * 1 2 Macrotermitinae 9 Macrotermes sp1 1 * 0 * * 0 * 0 * 0 * 0 * 0 * * 1 10 Macrotermes sp2 0 * 0 * * 0 * 0 * 0 * 0 * 1 * * 1 11 Odontotermes sp1 1 * 1 * * 0 * 0 * 1 * 0 * 0 * * 3 12 Ancistrotermes sp1 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1 3 Termitinae 13 Prohamitermes sp1 1 * 1 * * 1 * 1 * 0 * 0 * 0 * * 4 14 Labritermes sp1 0 * 0 * * 0 * 1 * 1 * 0 * 0 * * 2 15 Globitermes sp1 1 * 1 * * 0 * 0 * 1 * 0 * 0 * * 3 16 Globitermes sp2 1 * 1 * * 0 * 0 * 0 * 1 * 0 * * 3 17 Microcerotermes sp1 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 18 Microcerotermes sp1 0 * 1 * * 0 * 0 * 0 * 0 * 0 * * 1 19 Termes sp1 0 * 0 * * 1 * 0 * 0 * 0 * 0 * * 1 20 Termes sp2 1 * 0 * * 1 * 1 * 0 * 0 * 0 * * 3 21 Termes sp3 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 22 Homallotermes sp1 1 * 0 * * 0 * 1 * 0 * 0 * 0 * * 2 23 Homallotermes sp2 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 24 Microcapritermes sp1 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1 25 Procapritermes sp1 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 26 Procapritermes sp2 1 * 1 * * 0 * 0 * 1 * 0 * 0 * * 3

66 ANNEX III

Table 11. Termite species list per benchmark site

No. Family Species BS1 BS2 BS3 BS4 BS5 BS6 BS7 BS8 S9 BS10 S11 S12 BS13 S14 BS15 BS16 Total

27 Procapritermes sp3 1 * 0 * * 0 * 0 * 1 * 0 * 0 * * 2 28 Procapritermes sp4 1 * 1 * * 0 * 1 * 1 * 0 * 0 * * 4 29 Procapritermes sp5 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1 30 Procapritermes sp6 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1

31 Kemneritermes sp1 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 32 Coxocapritermes sp1 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 33 Coxocapritermes sp2 0 * 1 * * 0 * 0 * 1 * 0 * 0 * * 2 34 Pericapritermes sp1 1 * 0 * * 0 * 0 * 1 * 0 * 0 * * 2 35 Pericapritermes sp2 1 * 0 * * 0 * 0 * 0 * 1 * 0 * * 2 36 Pericapritermes sp3 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1 37 Dicuspiditermes sp1 1 * 1 * * 0 * 1 * 1 * 0 * 0 * * 4 38 Dicuspiditermes sp2 1 * 1 * * 0 * 1 * 1 * 0 * 0 * * 4 4 Nasutiterminitae 39 Havilanditermes sp1 0 * 0 * * 0 * 1 * 0 * 0 * 0 * * 1 40 Nasutitermes sp1 1 * 0 * * 1 * 0 * 0 * 0 * 0 * * 2 41 Nasutitermes sp2 1 * 0 * * 1 * 1 * 1 * 0 * 0 * * 4 42 Nasutitermes sp3 1 * 0 * * 0 * 0 * 0 * 0 * 0 * * 1 43 Nasutitermes sp4 0 * 0 * * 0 * 0 * 1 * 0 * 0 * * 1 44 Nasutitermes sp5 0 * 0 * * 0 * 1 * 0 * 0 * 0 * * 1 45 Bulbitermes sp1 1 * 1 * * 0 * 0 * 0 * 0 * 0 * * 2 46 Bulbitermes sp2 1 * 0 * * 0 * 0 * 0 * 0 * 0 * * 1 47 Hospitalitermes sp1 0 * 0 * * 0 * 1 * 0 * 0 * 0 * * 1 48 Hospitalitermes sp2 1 * 0 * * 0 * 0 * 0 * 0 * 0 * * 1

Total 30 * 21 * * 10 * 14 * 21 * 2 * 1 * * 99

67 ANNEX III

Table 12. 1. Seven landuse types selected for ant and other macrofaunal sampling in Pasir Mayang and adjacent areas of central Sumatra

Site Dominant vegetation General character GPS reference Site physical Soil Production, deadwood and coding form and botany and elevation litter

-2 BS 1 Intact rainforest A small area of pristine lowland forest on 01-04-47 S Slope 25% pH (H2O) 4.0 Green biomass 0.133 kg m Canopy height 21 m a moderately steep slope, well drained 102-06-02 E Aspect 7o C org 4.01% Litter 1.37 kg m-2 -103 plant species with closed stratified canopy and Pasir Mayang Soil depth >100 cm C/N ratio 14.3 Litter depth 10 cm -37 functional modi generally light understorey. Tree 76 m asl Upper slope Ca 1.65 Al sat 53.0** Dead wood 21.3 kg m-2 -75% crown cover buttresses and stilts present. S/S/C 62/24/14 -27.33 basal area* ECEC 7.91*** -50 trees plot-1

-2 BS 3 Secondary rainforest A ridge-top site contiguous with BS1 but 01-04-43 S Slope 12% pH (H2O) 4.5 Green biomass 0.045 kg m Canopy height 10 m logged-over with secondary regrowth on 102-05-55 E Aspect 150o C org 1.85% Litter 1.50 kg m-2 -50 plant species old log collection points and skid trails. Soil depth >100 cm C/N ratio 14.2 Litter depth 15 cm -20 functional modi Transects and pitfalls placed to run Pasir Mayang Ridge top Ca 1.55 Al sat 47.6** Dead wood15.8 kg m-2 35-% crown cover through secondary areas. Generally 85m asl S/S/C/ 54/8/38 -13.33 basal area* closed canopy but of limited stratification. ECEC 6.16*** -11 trees plot-1 High liana/creeper burden

-2 BS 6 Young Paraserianthes A heavily disturbed site with line planted 01-05-59 S Slope 20% pH (H2O) 4.4 Green biomass 0.247 kg m plantation 3/4 yrs sengon trees established after complete 102-06-43 E Aspect 202 o C org 2.78% Litter 1.78 kg m-2 Canopy height 6 m clearance. Canopy very open and the Pasir Mayang Soil depth >100 cm C/N ratio 16.4 Litter depth 3 cm -42 plant species ground with a heavy load of dead wood. 65 m asl Upper slope Ca 2.04 Al sat 41.5** Dead wood 14.90 kg m-2 -27 functional modi S/S/C 84/8/8 -40% crown cover ECEC 6.29*** -6.00 basal area* -8 trees plot-1

-2 BS 8 Rubber plantation, 8 yrs A mature monospecific plantation in 01-05-25 S Slope 3% pH (H2O) 4.6 Green biomass 0.107 kg m Canopy height 11 m current production for latex, located on a 102-07-05 E Aspect 183o C org 5.97% Litter 0.77 kg m-2 -68 plant species gentle slope upper to ridge top. Canopy Pasir Mayang Soil depth >100 cm C/N ratio 15.7 Litter depth 5 cm -37 functional modi closure complete and herb/understorey 53 m asl Gentle ridge top Ca 2.41 Al sat 15.7** Dead wood 7.67 kg m-2 -65% crown cover layers very sparse. Large decaying tree S/S/C 14/27/59 -14.67 basal area* trunks from previous forest clearance ECEC 9.94*** -14 trees plot-1 present with moderate dead wood load.

-2 BS 10 Jungle rubber, 15-38 yrs Mixture of old rubber trees still in 01-10-12 S Slope 0% pH (H2O) 5.2 Green biomass 0.033 kg m Canopy height 14 m production and secondary forest regrowth 102-06-50 E Aspect 0o C org 6.23% Litter 0.62 kg m-2 -115 plant species with high liana/creeper burden. About 25- Pancuran Soil depth >100 cm C/N ratio13.5 Litter depth 8 cm -47 functional modi 30 yrs. old, at end of cycle ready for Gading Flat ground Ca 2.37 Al sat 49.5 Dead wood 13.5 kg m-2 -50% crown cover felling. Canopy closure ± complete and 30 m asl S/S/C 6/70/24 -18.00 basal area* well stratified. Flat site , riverine. ECEC 10.72 -22 trees plot-1

-2 BS 12 Imperata cylindrica Large open ridge-top site devoid of trees 01-36-05 S Slope 5% pH (H2O) 5.8 Green biomass 0.227 kg m grassland: "alang-alang" with knee-high uniform stand of course 102-21-22 E. Aspect 225o C org 2.19% Litter 0.11 kg m-2 Canopy height 1 m grass. Little or no dead wood. Ground Kuamang Soil depth >100 cm C/N ratio 16.8 Litter depth 0.1 cm -11 plant species cracked and very hard. Kuning Ridge Ca 11.56 Al sat 22.4** Dead wood absent -10 functional modi 40 m asl S/S/C 66/14/20 -no trees ECEC 5.39***

-2 BS 14 Cassava garden, 10 yrs Open ridge-top site with line-planted 01-35-58 S Slope 0% pH (H2O) 5.0 Green biomass 0.207 kg m Canopy height 2 m cassava, about 2 yrs old. 102-21-11 E Aspect 0o C org 1.51% Litter 0.06 kg m-2 -15 plant species Weeded to prevent growth of other Kuamang Soil depth >100 cm C/N ratio 13.7 Litter depth 0.5 cm -12 functional modi vegetation. Ground very disturbed but Kuning Ridge top Ca 1.02 Al sat 46.0 Dead wood absent -50% canopy cover little or no dead wood. 48 m asl S/S/C 61/16/23 -no trees ECEC 4.76***

ANNEX III

Table 12. 2. Jambi all ant numerical density

Site Arithmetical Geometric mean, 95% confidence mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

BS1, Primary forest 352 23 1-1348 BS3, Logged over 522 239 28-2004 BS6, Paraserianthes 522 223 24-2065 BS8, Rubber 134 17 1-529 BS10, Jungle rubber 541 226 24-2123 BS12, Alang-alang 80 15 1-833 BS14, Cassava 48 6 1-131

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

Litter 102 52 20-134 0-10 cm 158 136 81-230 10-20 cm 47 24 8-68 20-30 cm 7 4 3-75

* back-transformed

Parametric ANOVAS:

One way: monoliths averaged across sites:

Between treatments (sites): F(6,28) = 2.10; ns .

Between strata: F (3,16) = 2.39; ns.

Two way: monoliths treated as replicates:

Between treatments (sites): F(6, 112) = 2.68; p<0.025>0.01.

Between samples (strata): F(3,112) = 5.89; p<0.001.

Interactions between sites and strata: F(18,112) = 0.76; ns.

69 ANNEX III

Table 12. 3. Jambi ant biomass

Site Arithmetical Geometric 95% confidence mean, g m-2 mean, g m-2 limits* (n=5) (n=5)*

BS1, Primary forest 0.346 0.024 0.004- 1.430 BS3, Logged over 0.285 0.189 0.049 - 0.721 BS6, Paraserianthes 4.889 0.350 0.012 – 9.862 BS8, Rubber 0.102 0.015 0.001 - 0.425 BS10, Jungle rubber 0.857 0.238 0.018 – 3.090 BS12, Alang-alang 0.030 0.010 0.002 - 0.157 BS14, Cassava 0.336 0.022 0.001 – 1.217

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, g m-2 g m-2 limits* (n=5) (n=5)*

Litter 0.076 0.035 0.010 - 0.122 0-10 cm 0.825 0.210 0.048 - 0.918 10-20 cm 0.079 0.034 0.010 - 0.116 20-30 cm 0.024 0.036 0.011 - 0.122

* back-transformed

Parametric ANOVAS:

One way, monoliths averaged across sites:

Between treatments (sites): F(6,28) = 1.22; ns .

Between strata: F (3,16) = 1.28; ns.

Two way: monoliths treated as replicates:

Between treatments (sites): F(6, 112) = 2.47; p<0.05>0.025.

Between samples (strata): F(3,112) = 3.81; p<0.025>0.01.

Interaction between sites and strata: F(18,112) = 0.75; ns.

70 ANNEX III

Table 12. 4. Jambi all termite numerical density

Site Arithmetical Geometric mean, 95% confidence mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

BS1, Primary forest 2892 971 190-4966 BS3, Logged over 163 65 12-364 BS6, Paraserianthes 512 47 1-1923 BS8, Rubber 128 11 2-201 BS10, Jungle rubber 211 25 2-1107 BS12, Alang-alang 3 2 0-8 BS14, Cassava 26 10 0-124

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

Litter 46 15 3-64 0-10 cm 106 80 43-148 10-20 cm 55 44 24-78 20-30 cm 49 4 1-50

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 4.064; p= 0.005

Betweeen strata: F (3,16) = 2.299; not significant.

71 ANNEX III

Table 12. 5. Jambi all termite biomass

Site Arithmetical Geometric mean, g 95% confidence mean, g m-2 m-2 limits* (n=5) (n=5)*

BS1, Primary forest 5.59 2.77 0.90-14.67 BS3, Logged over 0.09 0.10 0.01-0.26 BS6, Paraserianthes 0.59 0.47 0.01-1.50 BS8, Rubber 0.07 0.06 0-0.22 BS10, Jungle rubber 0.49 0.35 0-1.38 BS12, Alang-alang <0.01 <0.01 0-0.012 BS14, Cassava 0.02 0.02 0-0.06

Stratum level (all sites Arithmetical Geometric mean, g 95% confidence averaged) mean, g m-2 m-2 limits* (n=5) (n=5)*

Litter 0.08 0.06 0-0.14 0-10 cm 0.78 0.43 0.11-1.45 10-20 cm 0.07 0.06 0.03-0.11 20-30 cm 0.04 0.03 0-0.08

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 4.47; p= <0.025>0.001.

Betweeen strata: F (3,16) = 3.94; p <0.05>0.025.

72 ANNEX III

Table 12. 6. Jambi all macroarthropods numerical density

Site Arithmetical Geometric mean, 95% confidence mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

BS1, Primary forest 3668 2455 630-9120 BS3, Logged over 713 331 86-788 BS6, Paraserianthes 1312 630 184-2152 BS8, Rubber 397 346 177-679 BS10, Jungle rubber 830 512 219-1202 BS12, Alang-alang 86 30 2-429 BS14, Cassava 160 148 86-253

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

Litter 193 184 143-237 0-10 cm 517 343 153-767 10-20 cm 87 77 52-119 20-30 cm 69 38 14-111

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 7.22; p= 0.005

Betweeen strata: F (3,16) = 5.60; p <0.01.

73 ANNEX III

Table 12. 7. Jambi all macroarthropods biomass

Site Arithmetical Geometric mean, 95% confidence mean, g m-2 g m-2 limits* (n=5) (n=5)*

BS1, Primary forest 8.99 5.08 1.23-20.68 BS3, Logged over 1.89 1.82 1.09-2.80 BS6, Paraserianthes 5.79 2.13 0.12-7.79 BS8, Rubber 2.27 1.55 0.01-5.61 BS10, Jungle rubber 6.08 3.99 0.67-13.89 BS12, Alang-alang 0.64 0.44 0.01–1.76 BS14, Cassava 0.67 0.66 0.38-1.00

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, g m-2 g m-2 limits* (n=5) (n=5)*

Litter 1.39 1.12 0.62-1.97 0-10 cm 1.57 1.17 0.57-2.28 10-20 cm 1.73 0.23 0.12-0.34 20-30 cm 0.21 0.18 0.09-0.32

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 3.43 ; p= <0.05.

Betweeen strata: F (3,16) = 8.45; p <0.005.

74 ANNEX III

Table 12. 8. Jambi earthworms numerical density

Site Arithmetical Geometric mean, 95% confidence mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

BS1, Primary forest 3 2 1-8 BS3, Logged over 6 2 1-14 BS6, Paraserianthes 195 186 116-297 BS8, Rubber 35 6 1-123 BS10, Jungle rubber 576 565 428-743 BS12, Alang-alang 26 14 2-103 BS14, Cassava 102 53 12-228

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, nos m-2 nos m-2 limits* (n=5) (n=5)*

Litter 0 0 - 0-10 cm 109 82 47-138 10-20 cm 4 1 1-7 20-30 cm 0 0 -

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 12.31 ; p= <0.001

Betweeen strata: F (3,16) = 36.47; p <0.001.

75 ANNEX III

Table 12. 9. Jambi earthworm biomass

Site Arithmetical Geometric mean, g 95% confidence mean, g m-2 m-2 limits* (n=5) (n=5)*

BS1, Primary forest 0.032 0.01 0-0.046 BS3, Logged over 0.064 0.06 0-0.09 BS6, Paraserianthes 11.42 8.40 2.21-26.49 BS8, Rubber 0.77 0.53 0-2.18 BS10, Jungle rubber 60.16 33.59 11.92-91.81 BS12, Alang-alang 0.83 0.12 0.037-1.224 BS14, Cassava 4.67 2.79 1.11-11.03

Stratum level (all sites Arithmetical Geometric mean, 95% confidence averaged) mean, g m-2 gm-2 limits* (n=5) (n=5)*

Litter 0 - - 0-10 cm 10.80 9.67 7.82-11.88 10-20 cm 0.11 0.04 0-0.10 20-30 cm 0 - -

* back-transformed.

Parametric ANOVAs:

Between treatments (sites): F(6,28) = 20.4; p <0.001.

Betweeen strata: F (3,16) = 489.9; p<0.001.

76 ANNEX III

Table 12.10. Matrices showing differences between treatments (sites) by 7 single biotic parameters.

For each parameter, overall ANOVA is carried out by the non-parametric Kruskal-Wallis method and pairwise site comparisons by one-tailed Mann-Whitney. * p <0.05; ** p<0.025; *** p <0.005. Numbers in brackets refer to the sites. ns, not significant (p>0.05).

1. Ant abundance. H = 8.9, ns. BS1 BS3 ns BS6 ns ns BS8 ns ns ns BS10 ns ns ns *(10>8) BS12 ns *(3>12) *(6>12) ns *(10>12) BS14 ns *(3>14) *(6>14) ns *(10>14) ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

2. Ant biomass. H = 6.8, ns. BS1 BS3 ns BS6 ns ns BS8 ns *(3>8) ns BS10 ns ns ns ns BS12 ns **(3>12) *(6>12) ns *(10>12) BS14 ns ns ns ns ns ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

3. Termite abundance H = 14.64; p<0.025>0.01. BS1 BS3 **(1>3) BS6 ns ns BS8 **(1>8) ns ns BS10 **(1>10) ns ns ns BS12 ***(1>12) **(3>12) *(6>12) ns ns BS14 ***(1>14) ns ns ns ns ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

4. Termite biomass. H = 16.49; p<0.025>0.01. BS1 BS3 *(1>3) BS6 ns ns BS8 ***(1>8) ns ns BS10 *(1>10) ns ns ns BS12 ***(1>12) ***(3>12) *(6>12) ns ns BS14 ***(1>14) ns ns ns ns ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

77 ANNEX III

Table 12.10. Matrices showing differences between treatments (sites) by 7 single biotic parameters.

5. All macroarthropod abundance. H = 21.4; p<0.005. BS1 BS3 **(1>3) BS6 ns ns BS8 **(1>8) ns ns BS10 **(1>10) ns ns ns BS12 ***(1>12) ns **(6>12) **(8>12) **(10>12) BS14 ***(1>14) ns *(6>14) **(8>14) ***(10>14) ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

6. All macroarthropod biomass. H = 15.37; p <0.025>0.01. BS1 BS3 *(1>3) BS6 ns ns BS8 ns ns ns BS10 ns ns ns ns BS12 **(1>12) ns *(6>12) *(8>12) *(10>12) BS14 ***(1>14) **(3>14) ns ns ***(10>14) ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

7. Earthworm abundance. H = 24.0; p<0.005. BS1 BS3 ns BS6 ***(6>1) ***(6>3) BS8 ns ns **(6>8) BS10 ***(10>1) ***(10>3) ***(10>6) ***(10>8) BS12 *(12>1) ns ***(6>12) ns ***(10>12) BS14 **(14>1) **(14>3) ns ns ***(10>12 ns BS1 BS3 BS6 BS8 BS10 BS12 BS14

8. Earthworm biomass. H = 25.05; p<0.005. BS1 BS3 ns BS6 ***(6>1) ***(6>3) BS8 ns ns ***(6>8) BS10 ***(10>1) ***(10>3) ns ***(10>12) BS12 *(12>1) ns ***(6>12) ns ***(10>12) BS14 ***(14>1) **(14>3) *(6>14) ns **(10>14) *(14>12) BS1 BS3 BS6 BS8 BS10 BS12 BS14

78 ANNEX III

Table 12. 11. Site species lists for ants. Pitfall and monolith data combined.

Taxon BS 1 BS 3 BS 6 BS 8 BS 10 BS 12 BS 14 Total sites Dorylinae Dorylus laevigatus Smith X 1 Formicinae Anoplolepis longipes Jerdon X X X 3 Camponotus gigas Mayr X X X X X 5 Camponotus sp. 1 X X X 3 Colobobsis sp. X X 2 Paratrechina sp. 1 X X 2 Paratrechina sp. 2 X X X X X 5 Paratrechina sp. 3 X X 2 Polyrachis enermis Fr. Smith X 1 Polyrachis nigripilosa Mayr X X X 3 Polyrachis tyranica Smith X X 2 Polyrachis sp. 1 X 1 Polyrachis sp. 2 X 1 Myrmecinae Acanthomyrmex ferox Emery X X X 3 Cataulacus horridus Fr. Smith X 1 Crematogaster sp. 1 X X X X 4 Crematogaster sp. 2 X X X X 4 Crematogaster sp. 3 X X X X 4 Crematogaster sp. 4 X X X X 4 Crematogaster sp. 5 X X 2 Crematogaster sp. 6 X 1 Crematogaster sp. 7 X 1 Lophomyrmex betodi Emery X X X 3 Myrmecina sp. X 1 Myrmicaria sp. X 1 Pheidole sp. 1 X X X 3 Pheidole sp. 2 X X X X 4 Pheidole sp. 3 X X X X X 5 Pheidole sp. 4 X X X X 4 Pheidole sp. 5 X X X X X 5 Pheidolegeton sp. X X X 3 Proatta buteli Forel X X X 3 Strumigenys sp. X 1 Tetramorium bicarinatum X X X X X 5 Ponerinae Anochetus sp. 1 X 1 Anochetus sp. 2 X X X X 4 Brachiponera liteipes Mayr X X X 3 Diacamma intricatum Fr. Smith X 1 Diacamma vagans Fr. Smith X 1 Myopias sp. Mystrium sp. X 1 Odontomachus rixosus Fr. Smith X X X X X 5 Odontomachus sp. X 1 Odontoponera nitens Creigaton X X 2 Odontoponera transversa Fr. Smith X X X 3 Pachycondyla sp. 1 X X 2 Pachycondyla sp. 2 X 1 Leptanillinae Protanilla sp. X 1

79 ANNEX III

Table 12. 11. Site species lists for ants. Pitfall and monolith data combined.

Taxon BS 1 BS 3 BS 6 BS 8 BS 10 BS 12 BS 14 Total sites Pseudomyrmicinae Teraponera sp. 1 X 1 Teraponera sp. 2 X Cerapachyinae Cerapachys sp. X 1 Dolichoderinae Dolichoderus sp. 1 X 1 Dolichoderus sp. 2 X 1 Tapinoma sp. 1 X 1 Tapinoma sp. 2 X X 2 Technomyrmex sp. 1 X X 2 Technomyrmex sp. 2 X X 2 Total species richness 16 18 24 16 33 15 9 (all sites) 57

Higher taxonomic richness (number of subfamilies) was:

• BS1: 3 • BS3: 3 • BS6: 5 • BS8: 5 • BS10: 6 • BS12: 4 • BS14: 3

80 ANNEX III

Table 13. Primary data catalogue for intensive biodiversity baseline study, Jambi, Central Sumatra

Dir. Sub-dir Sub-sub-dir File Name Author Content

RBA - - CATALOG.DOC A. Gillison*, N. Liswanti* Primary data catalogue for intensive biodiversity RBA-LIST.DOC List of participants RBA survey PLOTSMAP.DOC A.Gillison*, D. Sheil A way to Sumatra plots map OBJECTIV.DOC Baseline study for biodiversity assessment in Jambi Site - JBSSITE.XLS A. Gillison*, N. Liswanti* Site physical data

Soil - JBSSOIL.XLS M van Noordwijk*, K. Hairiah Soil analytical data (including carbon stock) Macrofauna EARTHSUM.XLS F.X. Susilo*; S. Hardiwibowo Earthworm data MONOLITH.XLS Monolith data 11 taxonomic group MONOSUMM.XLS Summary of monolith data 11 taxonomic group MONOSPTT.XLS Total taxa monolith data 11 taxonomic group PITFALL.XLS Pitfall data 11 taxonomic group PITFSUMM.XLS Summary of pitfall data

Animal Insect1-A AW-FIG.DOC A. Watt*; P. Zborowski; Insect Figure AW-REP.DOC O. Rachmatsyah; C.H. Noor; Insect preliminary report by AW JBSINSTT.XLS Wardhana; I. Setiawan Summary of total insect species Insect preliminary report by OR Complete beetle data from PB Summary beetle data (based on trophic level) Insect2-B ANTBIOMA.XLS D.E. Bignell*; D. Jones; Biomass of litter and soil ants ANTNUMER.XLS F.X. Susilo Abundance of litter and soil ants ANTSUMMA.XLS Summary of ant species DB-REPO.DOC Ant preliminary report (second version) by DB Birds JBSBIRD1.XLS P. Jepson*; Djarwadi Birds collection data 1 BIRDSUMM.XLS Summary of bird species JBSBIRD2.XLS Birds collection data 2 PJ-REPO.DOC Bird preliminary report by PJ PRE-REPO.DOC Back From Field Report of Bird Surveys Mammals BIGMMALS.XLS I. Maryanto*; M.H. Sinaga Big mammals data MAREPO-E.DOC A. Kartono Mammals preliminary report (English) by LIPI MAREPO-I.DOC Mammals preliminary report (Indonesia) by LIPI SUMBIGMA.XLS Summary of big mammals species SUMMALS.XLS Summary of small mammals species Termite TERMSUMM.XLS D. Jones*; D. Bignell; Summary of termite species DJ-REPO.DOC F.X. Susilo Termite preliminary report by DJ (hard copy)

Plants - JBSPFA.MDB Suhardjono*; Afriastini; Site physical and PFA data in access format JBSREPVE.DOC A. Gillison*; N. Liswanti*; Vegetation preliminary report by LIPI JBSSPPTT.XLS E. Permana Vegetation species data JBSSUMVE.XLS Summary of vegetation data SITELOC.XLS Site location and vegetation structural data

Map - LISTMAP.DOC A. Gillison*, N. Liswanti* List all of map (vegetation, soil, geology, contour and site location). Graphic - JAMALL..PPT A. Gillison*, N. Liswanti* Digital elevation model of Jambi site JAMTERM.PPT Regression plot of termite abundance & basal area Of woody plant over 7 land use system TERMSPMD.PPT Ratio of plant species richness to plant functional types as indicator of termites species richness. TERMABUN.DOC Termite abundance along a land use gradient JBSTERM.PPT Termite Species Richness along a land use gradient measured against (I) Total Unique Modi; (ii) Total Plant Species; (iii) Spp/Modi JBSCSTOCK.PPT Comparative relationships between above-ground carbon, plant functional type richness, species richness and species / modi ratios along a gradient of land use types, Jambi, Lowland Sumatra Note: * Principal source of data

81 ANNEX IV: LIST OF ACRONYMS

ACIAR Australian Centre for International Agricultural Research, Australia

AEZ Agro Ecological Zone

ASB Alternatives to Slash and Burn, Project (ICRAF)

BIOTROP SEAMEO Regional Centre for Tropical Biology, Indonesia

CIFOR Center for International Forestry Research, Indonesia

CSIRO Commonwealth Scientific and Industrial Research Organization, Australia

EPHTA Ecoregional Program for the Humid and Sub-humid Tropics of Sub- Saharan Africa

GCTE Global Change in Terrestrial Ecosystems

GEF Global Environmental Facility, USA (World Bank, UNDP and UNEP)

ICRAF International Centre for Research in Agroforestry, Nairobi

ICSEA Southeast Asian Impacts Centre

IFPRI International Food Policy Research Institute, Washington DC, USA

LIPI Indonesian Institute of Science, Indonesia (Lembaga Ilmu Pengetahuan Indonesia)

NARS National Agricultural Research Service/System/s

SEAMEO Southeast Asian Ministries of Education Organisation, Thailand

UNDP United Nations Development Programme, New York, USA

UNEP United Nations Environment Programme, Kenya

USAID Inited States Agency for International Development, Washington DC, USA

82 ANNEX V

List of Maps

Map 1. Elevation and Site Locations of Biodiversity Baseline Study in Pasir Mayang and Kuamang Kuning Area, Jambi Province. Scale 1:50.000. Source: Bakosurtanal

Map 2. Vegetation and Site Locations of Biodiversity Baseline Study in Pasir Mayang and Kuamang Kuning Area, Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 3. Geology and Site Locations of Biodiversity Baseline Study in Pasir Mayang and Kuamang Kuning Area, Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 4. Soils and Site Locations of Biodiversity Baseline Study in Pasir Mayang and Kuamang Kuning Area, Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 5. Vegetation and Site Locations of Biodiversity Baseline Study in Pasir Mayang Area (BIOTROP site study), Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 6. Geology and Site Locations of Biodiversity Baseline Study in Pasir Mayang Area (BIOTROP site study), Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 7. Elevation and Site Locations of Biodiversity Baseline Study in Pasir Mayang Area (BIOTROP site study), Jambi Province. Scale 1:50.000. Source: Bakosurtanal.

Map 8. Soils and Site Locations of Biodiversity Baseline Study in Pasir Mayang Area (BIOTROP site study), Jambi Province. Scale 1:50.000. Source: Bakosurtanal.

Map 9. Vegetation and Site Locations of Biodiversity Baseline Study in Kuamang Kuning Area (ICRAF benchmark study), Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 10. Geology and Site Locations of Biodiversity Baseline Study in Kuamang Kuning Area (ICRAF benchmark study), Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.

Map 11. Elevation and Site Locations of Biodiversity Baseline Study in Kuamang Kuning Area (ICRAF benchmark study), Jambi Province. Scale 1:50.000. Source: Bakosurtanal

Map 12. Soils and Site Locations of Biodiversity Baseline Study in Kuamang Kuning Area (ICRAF benchmark study), Jambi Province. Scale 1:1.000.000. Source: Y. Laumonier, et. al. 1986.