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There is currently much speculation about the consequences of human-generated disturbance on tropical rainforest biodiversity, particularly impacts on species composition (Whitmore & Sayer 1992; Laurance & Bierregaard 1997) and the possibility of irreversible changes in tropical forests following logging (Reid & Miller 1989; Reid 1992). Over recent decades, the harvesting of timber has become the major source of human disturbance in tropical forests. The total loss in tropical forests in the decade 1981–90 was 154 million ha, representing an annual loss of 0·81% of the total forest cover in 1980 (FAO 1993, 1995), with tropical moist forests counting for the largest forest area lost (85% of the total; Whitmore 1997). Not all tropical rainforests are cleared through deforestation, and selective logging constitutes a substantial impact on remaining forests, especially in Asia. From 1981 to 1990, 5·6 million ha were logged each year, of which 2·6 million ha were in the Americas, 2·1 million ha in Asia and 0·9 million ha in Africa, representing 0·3%, 0·7% and 0·2% of the respective forest areas (Whitmore 1997). Within Asia the greatest logging impact was on forests in the Malay Archipelago (which includes the island of Borneo), where 1·8 million ha year−1 were logged (compared with 0·4 million ha year−1 on the continent of Asia). In 1989 it was estimated that only 0·1% of remaining natural tropical forest was under active sustainable management and only 3% set aside for the conservation of biodiversity (Holmberg, Bass & Timberlake 1991).
South-east Asia has the tallest tropical rainforests in the world (some emergent trees can reach 70–80 m) with the greatest timber volumes (dominated by the Dipterocarpaceae), which accounts for the high levels of logging in this region. This is particularly true for forests in Borneo, where current fellings in the primary forest in Borneo may, over limited areas, yield greater than 100 m3 ha−1 compared with the regional average of 45 m3 ha−1 (Collins, Sayer & Whitmore 1991). Borneo, at the heart of Sundaland, is a centre of biological diversity, with a very rich flora and fauna (e.g. 265 of the 390 species of dipterocarps are found there; Collins, Sayer & Whitmore 1991). In terms of forests throughout Malaysia, rainforests cover 200 450 km2, of which 36 000 km2 are found in Sabah (Sarawak being the other Malaysian state in Borneo). Management of forests in East Malaysia (Malaysian Borneo) is under state control, although under overall Federal policy (Sabah Forestry Department 1986; FAO 1987). In 1992, about 60% of Sabah’s total land area (73 710 km2) was under forest cover of some sort, with 45% under Permanent Forest Estate (PFE) and 3·3% in State Parks (Marsh 1995). Of the PFE, 2530 099 ha were classified as Commercial Forest Reserve (i.e. can be exploited for logging; Marsh 1995). In 1974 it was estimated that 6·4 million ha (88% of the total land area) was forested, whereas only 4·5 million ha remained in 1985 (i.e. a 30% reduction in 11 years; Marshall 1992). The main cause of degradation and deforestation in Sabah has been from logging: between 1985 and 1990 the annual deforestation rate was 800 km2 compared with 2210 km2 for the whole of Federal Malaysia. Sabah’s commercial forest has steadily been depleted over the past three decades: by 1997 only 365 879 ha of virgin forest remained within the Commercial Forest Reserve, compared with 2·7 million ha in 1970 (Chai 1997). Undisturbed lowland dipterocarp forest is becoming particularly scarce, and rapidly being diminished by logging (Marsh & Greer 1992).
Species diversity in tropical rainforests
Plant ecology has for many years described ecological processes in terms of changes along gradients (Austin 1985). In rainforest, predictability of disturbance (e.g. tree-fall gap formation) has been shown to be an important factor in vegetational differentiation of closely related species across microsites (Ashton, Gunatilleke & Gunatilleke 1995; reviewed by Brown & Jennings 1998). The distribution of animals in tropical rainforests has also long been linked with environmental factors (Allee 1926; Janzen & Schoener 1968), and predictable environmental gradients in the forest landscape (i.e. river edge to forest interior) may be expected to provide a strong correlative base to insect distribution patterns in primary rainforest, just as it has for plant communities. Species distributed along environmental gradients may be divided into discrete associations linked to particular biotopes (sensuWhittaker, Levin & Root 1973) in the landscape. We use the terms ‘habitat’ and ‘biotope’ according to the definitions of Samways (1994), i.e. where habitat is an autecological concept emphasizing the interaction between the species and the physical habitat structure, and biotope refers to the physical local area where a species, or association of species, lives (Samways 1994). We also make a distinction between ‘association’ and ‘assemblage’: an association is a group of species showing high correlation in abundance pattern across biotopes, and sometimes showing high fidelity to a particular biotope, whereas an assemblage is the mixture of species from different associations found in any particular biotope (or sample). Vertical environmental gradients have been studied in rainforest, correlated to changes in microclimate from the ground to canopy (Allee 1926; Kato et al. 1995; Davis et al. 1997), and natural breaks in the forest created by large rivers may provide similar extreme conditions to those found in the upper forest canopy (Davis & Sutton 1998). Here we suggest that examining species distributions across natural environmental gradients in primary forest may be a useful way of looking at, and understanding, species distributions in disturbed ecosystems.
Although ecologists are graduating from a study of pattern to a study of process and ecosystem function (Hanski 1989; Didham et al. 1996), there is still a great ignorance of pattern in tropical forest insect communities, particularly in relation to ecosystem disturbance through such events as logging (Sutton & Collins 1991). Para-taxonomic approaches, such as insecticidal fogging, produce extensive species lists and are an effective way of tackling the problem of insect super-diversity (Stork 1991), but, because of the large number of organisms collected, pose problems in interpreting underlying patterns. These studies present particular problems when examining the effects of disturbance. Similarly, whole community surveys, while currently much vaunted, often succumb to the same problem (Robinson et al. 1992), where inferences about overall effects of disturbance are difficult to make (see arguments by Crome 1997). To counter these problems, specific groups of organisms (indicators) can be singled out for special attention, and examined in detail (Pearson 1994; McGeoch 1998). The relative merits of various indicator groups, and their uses in studying the effects of disturbance, have been discussed extensively in the literature (Landres, Verner & Thomas 1988; Andersen 1990; Brown 1991; Holloway & Stork 1991; Sutton & Collins 1991; Kremen 1992; Halffter & Favila 1993; Kremen et al. 1993; Sparrow et al. 1994; Brown 1997; Crome 1997; Dufrêne & Legendre 1997; Lawton et al. 1998; McGeoch & Chown 1998; McGeoch 1998; New 1998).
Dung beetles are important decomposer organisms, involved with nutrient recycling, seed dispersal and the control of vertebrate parasites (by removal of source of infection), and are therefore an important component of tropical forest systems (Hanski & Krikken 1991). The local distribution of dung beetles is strongly influenced by vegetation cover and soil type (Nealis 1977; Doube 1983; Janzen 1983), and the physical structure of the forest appears to be an important determining factor in the composition and distribution of dung beetle assemblages (Davis & Sutton 1998). Consequently, dung beetles are a useful indicator group because they reflect structural differences (i.e. architectural, abiotic) between biotope types; thus, they differ from insects that reflect floristic differences (i.e. species composition, biotic) through biotope fidelity via plant-feeding specializations (e.g. moths and butterflies). Dung beetles have been used in several studies to investigate the effects of environmental disturbance on forest diversity and structure (Howden & Nealis 1975; Klein 1989; Nummelin & Hanski 1989; Halffter, Favila & Halffter 1992; Hill 1995; Davis & Sutton 1998; Davis 2000a). The rationale for using dung beetles as indicators of disturbance has been reviewed by Halffter & Favila (1993). Groups where interspecific competition is strong, such as the dung beetles (Hanski & Cambefort 1991a), may be expected to show species associations with a high degree of fidelity to a particular biotope. Work in Australia has demonstrated high degrees of biotope specificity in dung beetle species between rainforest and more open areas (Hill 1996). If the distribution of biotopes in the landscape changes through disturbance, dung beetle assemblage structure can be expected to mirror these changes.
In this study we examined the effects of logging and conversion to plantation forest on rainforest dung beetle assemblages. Dung beetle taxonomy, behaviour and ecology have been studied extensively in South-east Asia (Hanski 1983; Hanski & Krikken 1991), providing valuable information for such studies. Previous research into the effects of habitat disturbance on dung beetles has shown that forest clearance reduces species richness (Howden & Nealis 1975) and that habitat fragmentation reduces richness and abundance (Klein 1989). However, studies on the effects of logging have been carried out where logging intensity was relatively low (Nummelin & Hanski 1989; Nummelin 1996) and where effects were negligible, or the total sample size was low and sampling effort insufficient to reveal more than very gross underlying patterns (Holloway, Kirk-Spriggs & Chey 1992). There are few published studies on the effects of conversion to plantation forest on tropical insect communities (Holloway, Kirk-Spriggs & Chey 1992; Chey, Holloway & Speight 1997; Davis, Huijbregts & Krikken 2000). As far as we are aware, only one previous published report has examined the effects of conversion to plantation on dung beetle assemblages, where species richness and diversity was seen to be reduced in plantation forest but no other patterns were discernible (Nummelin & Hanski 1989). There has been one previous publication relating to the effects of plantation conversion on dung beetle assemblages relating to this current study (Davis, Huijbregts & Krikken 2000).
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The material analysed here represents 86 species from 35 279 identified specimens. Application of richness estimators gave predicted species numbers of 78 (Chao 1) and 79 (Chao 2) for the pitfall data and 88 (Chao 1) and 85 (Chao 2) for the FIT data. These results suggest that FIT give a more comprehensive sample of the beetle species present in a given area. The species richness and composition of the full collection from Danum stands at 97 species from 68 481 specimens (Davis 2000b). Because of problems with taxonomy, the data analysed excluded the genera Aphodius Illiger and Panelus Lewis; both genera exist on the margins of dung beetle communities in the Bornean rainforest (Cambefort 1991; Hanski & Cambefort 1991b), therefore these omissions should not affect analysis of community structure. Species in these genera represented seven of the total of 97. A further four species were rare and not collected in the main trapping programme analysed here.
Both pitfall and FIT data showed clustering of species into several distinct associations, each association having a distinct ecological character or shared habitat. Pitfall data (Fig. 1) had a higher resolution and greater degrees of freedom than the FIT data (Fig. 2) as more traps were used in the pitfall sampling, and more biotopes sampled, than in the FIT collections. Associations could broadly be defined as riverine, interior-primary (although in figures this is simply referred to as primary) and ‘even’ (evenly distributed across biotopes). The interior-primary forest species were divided into two distinct associations (P1 and P2). Some species lay between these associations (riverine and interior-primary) and were therefore intermediate in ecological character. Pitfall data showed a greater degree of clumping, with the riverine association made up of river edge (R1), river bank (R2) and riverine non-edge/bank (R3) components. The river edge component was located at the very margin of the forest, whereas the river bank association was found under forest cover by the river.
Figure 1. R-mode single-link cluster analysis dendrogram and linkage diagram for dung beetle species represented by two or more individuals, collected by baited pitfall trap from primary forest traps in the Danum Valley Conservation Area. Species associations recognized from clusters in the linkage diagram are indicated on the dendrogram by the same symbol in both diagrams. The black square symbol represents species that form part of a general riverine cluster (R1 + R2 + R3) but cannot be directly attributed to any one riverine association. Numbers indicate species as in Appendix 1. In the linkage diagram, linkages of 90%+ similarity are indicated by thick solid lines, those of 80–89% by solid lines, and those of 75–79% by broken lines. Dotted lines with arrows indicate linkages below 75% in a minimum spanning tree. Line length has no significance, and the points are distributed to ensure the diagram is clear, rather than being placed through an ordination method. Three species, represented by a small number of individuals, cannot be attributed to an association (i.e. 24, 39 and 45) and these outlying species are not included in linkage diagram.
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Figure 2. R-mode single-link cluster analysis dendrogram and linkage diagram for dung beetle species represented by two or more individuals, collected by flight intercept trap from primary forest traps in the Danum Valley Conservation Area. Format as for Fig. 1 but numbers indicate species in Appendix 2.
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The pitfall and FIT results showed much concordance in assignation of species (Table 2) except with regard to the even associations. The general riverine association of the FIT analysis shared 14 species with the three pitfall riverine associations. Only one species, Onthophagus aphodioides Lansberge, was classed as riverine in the FIT analysis and interior-primary in the pitfall analysis. Six of the FIT interior-primary species fell into the pitfall interior-primary associations, and only one into the even association. However, five of the FIT even species were placed in the larger pitfall interior-primary association. An R-mode analysis including both pitfall and FIT data was not attempted as it would have yielded some clusters influenced by methodological bias. In subsequent discussion, greater emphasis will be placed on the pitfall results because of the greater number of samples involved.
Table 2. Comparison of species caught by pitfall trap and flight intercept trap (FIT), showing degree of concordance between trapping methods. Species are divided into the associations demonstrated in Figs 1–4. Concordance is indicated in bold. The main difference is in assignation of species to the even categories. See Figs 1 and 2, and text, for description of each association
| || ||FIT|
| ||R1||2|| || || |
| ||R2||3|| || || |
|Pitfall||R3||9|| || || |
| ||Even||3||1||1|| |
| ||P2|| || ||1||1|
Figures 3, 4 and 5 show species abundance distributions in various biotope samples for each association identified through cluster analysis (i.e. riverine, interior-primary and even associations). In this way, the whole assemblage of species was divided into ecological associations, and each association could then be examined independently. For clarity, curves in these and other figures are displaced along the x-axis rather than superimposed. Data were taken from primary (riverine and interior forest), logged and plantation forests. Figure 3 clearly shows the differing behaviour of each association along the environmental gradient, with the highest abundance and species richness recorded from the transect representing the ‘core biotope’ of each association, with the exception of the even association which showed little change in the rank–abundance curves. For example, species abundance and richness within the riverine non-edge association (Fig. 3b) were greatest in the riverine B transect, which represented the core biotope for this association. The riverine-edge association showed a dramatic decline in richness and abundance from riverine into interior-primary forest, whereas the other associations were better represented on all transects, and spread into sites away from their core biotope, but with decreased richness and abundance in all cases (with the exception of the even association, as already mentioned). Pitfall data from disturbed forests (Fig. 4) showed that all associations were represented, but this representation varied in each assemblage (sample). All logged forest transects varied from the primary forest samples shown in Fig. 3a. The riverine-edge association (R1) was not strongly represented, or was entirely absent, in interior-primary transects (primary B and primary C; Fig. 3a), but was well represented in plantation transects and logged site D and present in logged site B, suggesting that this association had spread beyond its natural compass in these forests. All logged forest sites supported a well-developed riverine non-edge association (Fig. 4b), closely resembling the curves from riverine non-edge habitat in pristine forest (i.e. 10–50 m from the river, riverine transect B; Fig. 3b), as did plantation transect A. The interior-primary association (Fig. 4d) showed a reduction in richness and abundance in all disturbed transects compared with the core biotope curves in Fig. 3d, with the greatest reduction in plantation forests. The curves for the even association (Fig. 4c) in general showed a reduction in species richness, most pronounced in plantation forest, although the general shape of each curve was similar to those in Fig. 3c. Figure 5 for the FIT data shows the same trends shown in Figs 3 and 4, with a well represented (in terms of species richness and abundance) riverine association in logged forest and a reduced interior-primary association in both logged and plantation forest, although resolution was poorer due to the lower number of sites sampled than the pitfall collections. It is of interest that the shape of the curves for all associations tended to be maintained across all biotopes for the pitfall data (much less so for the FIT data) except where abundance and richness were drastically reduced.
Figure 3. Rank–abundance curves for samples of dung beetles collected by baited pitfall trap from primary forest in the Danum Valley Conservation Area in Sabah. Species are divided into associations identified by cluster analysis (Fig. 1), separated along an environmental gradient from riverine-edge habitat (riverine A; Table 1), riverine non-edge (riverine B), to deep interior forest (primary C). Each curve represents samples from a different transect (Table 1); the symbols used replicate, for convenience, those shown in other figures (Figs 1 and 2, 7 and 8) but here represent transects and not the associations. For clarity, curves are displaced along the x-axis rather than superimposed. Associations are: (a) riverine-edge association (R1); (b) riverine non-edge association (R2 + R3); (c) even association; (d) interior-primary association (P1 + P2). Transects: open circles, riverine A; closed circles, riverine B; open triangles, primary A; closed triangles, primary B; open squares, primary C.
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Figure 4. Rank–abundance curves for samples of dung beetles collected by baited pitfall trap from logged and plantation forest in the Ulu Segama Reserve, Sabah. Species are divided into separate associations following the same method employed in Fig. 3. Associations are: (a) riverine-edge association (R1); (b) riverine non-edge association (R2 + R3); (c) even association; (d) interior-primary association (P1 + P2). Transects: open circles, logged D; closed circles, logged A; open triangles, logged B; closed triangles, plantation B; open squares, plantation A.
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Figure 5. Rank–abundance curves for samples of dung beetles collected by flight intercept trap from primary, logged and plantation forest in the Ulu Segama Reserve, Sabah. Species are divided into associations identified by cluster analysis (Fig. 2), separated along an environmental gradient from riverine-edge habitat (riverine B; Table 1) to deep interior forest (primary B). Each curve represents samples from a different trap (Table 1). Associations are: (a) riverine association (see Fig. 2); (b) even association; (c) interior–primary association. Transects: open circles, riverine B; closed circles, primary A; open triangles, primary B; closed triangles, logged D; open squares, plantation B.
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When individuals in each association were pooled from across all samples, the shape of the major pitfall association curves varied considerably (Fig. 6a), with the main riverine association being distinctly sigmoid, with some dominance, but with greater packing or evenness of middle-ranking species than in the other associations. Consistency of curve shape between pooled samples and individual samples for each association suggested pooling of samples in this way was valid, and followed the same principle as combining samples through time (e.g. accumulation of insects in a trap over several days) or space (e.g. the initial pooling of samples from individual pitfalls in each transect). The even and interior-primary associations had more linear curves but the latter showed greater equitability, packing in more species over a lesser abundance range (Fig. 6a). The curves from the combined FIT data were more similar (Fig. 6b), also being somewhat sigmoid. However, the FIT data did not separate the two small R1 and R2 associations of the pitfall analysis, and five of the FIT even association species were assigned to the interior-primary association in the pitfall analysis (Fig. 6b). These differences could be reconciled by (i) combining the R1, R2 and R3 clusters (Fig. 6b) to form a riverine association equivalent to the riverine association in Fig. 6a, and (ii) adding species that were in even and P2 associations in the FIT analysis, but assigned to P1 in pitfall analysis, to the P1 curve in Fig. 6b to create an ‘augmented P1 curve’ equivalent to P1 in the pitfall analysis (Fig. 6a).
Figure 6. (a) Species abundance curves, from pitfall trap data, obtained by pooling individuals in each association from across all the samples, both primary and disturbed. Species represented in the total sample by five individuals or less are assigned subjectively to the associations with which their distribution coincides most closely, as are those included in the analysis but placed as outliers or intermediates. Riverine associations are shown both separately (R1, R2, R3) and combined (R1 + R2 + R3). The combined riverine curve is equivalent to riverine curve of the flight intercept trap analysis (Fig. 6b). (b) Species abundance curves, from flight intercept trap data, obtained as in (a). Species in even and P2 associations with an asterisk can be assigned to P1 in pitfall analysis: these are added to the P1 curve here to create an ‘augmented’ P1 curve (far right of figure) that is therefore equivalent to P1 in pitfall analysis (Fig. 6a).
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In disturbed forests, pitfall data (Fig. 7) showed that interior-primary forest clusters (P1 and P2) from the earlier analysis (Fig. 1) had persisted relatively unchanged, as had the even cluster. The riverine clusters, however, had been disrupted, with the development of a new, loosely associated, cluster (encircled star; Fig. 7) consisting of species that were common in plantation forests and moderately represented in logged ones. These species were drawn from all three riverine associations but also with one even association species and two (39, 45) that were placed as outliers in Fig. 1 (i.e. not assigned to an association), being represented by only a small number of individuals in the undisturbed biotopes. Remnants of the old riverine associations persisted, with one species from R1 and three from R2 showing little penetration of disturbed biotopes. Those from R3 and the even association were more strongly represented in all disturbed biotopes, whereas representation of P1 and P2 was weaker and concentrated in the logged ones.
Figure 7. Single-link dendrogram and linkage diagram for an R-mode analysis of species in all pitfall samples. Conventions are as in Fig. 1, including small symbols representing membership of clusters recognized in the earlier analysis (associations: closed upward triangle, R1; closed downward triangle, R2; open square, R3; open upward triangle, even; closed circle, P1; open circle, P2). Solid lines indicate links of 80% and above, broken ones those of 70–79%. Dotted lines indicate links of lower values that unite the various clusters or bring in outliers (part of the minimum spanning tree). Smaller symbols for species in the ‘encircled star’ cluster indicate from which undisturbed cluster (Fig. 1) the species are drawn, the small dots indicating outliers in Fig. 1. Histograms indicate the average percentage representation across the samples of species in each cluster (these also give an indication of proportional representation across the samples from undisturbed biotopes). In the linkage diagram, a new cluster has formed (encircled star) consisting of species that are common in plantation biotopes and moderately represented in logged ones, formed largely by the breakdown of the riverine clusters (Fig. 1). The histograms are placed approximately opposite the relevant cluster in the dendrogram, with larger symbols enabling the clusters to be located in the linkage diagram. The bottom histogram indicates the blocks for riverine (R), interior-primary (Pr), logged (L), plantation (Pl) and cocoa (C) samples.
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A similar pattern emerged in an analysis performed on the full suite of FIT samples, illustrated in Fig. 8. The P1 and P2 clusters from Fig. 2 largely persisted (one P2 species was displaced). The even cluster was divided into two, with a cluster of four species showing little penetration of disturbed biotopes, and a larger cluster of eight that was well represented in the sample from logged forest. Only one species in the smaller cluster was included in the pitfall analyses, and that was placed in the P1 cluster although in a somewhat intermediate position between it and the even cluster. The large riverine cluster of Fig. 2 had disintegrated, with a group (solid square) showing little penetration of the disturbed biotopes, but the others showing it in varying degrees, such as moderate, strong to predominant (closed, open and encircled stars) in the logged sample, or strong in the cocoa sample (open square).
Figure 8. Single-link dendrogram and linkage diagram for an R-mode analysis of species in all flight intercept trap samples. Conventions are as in Figs 1, 2 and 7. Solid lines in the linkage diagram indicate links of 85% and above, broken lines those of 75–84%, and dotted lines those of lower value that are part of the minimum spanning tree uniting the clusters. Three new clusters are formed (closed star, open star and encircled star), formed largely by the breakdown of the riverine and even clusters (Fig. 2). The bottom histogram indicates the blocks for riverine (R), interior-primary (P), logged (L) and cocoa (C) samples.
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There were some differences compared with the pitfall result, particularly in the composition of the most disturbed clusters (encircled stars) of each. Both included species 57 and 84, but 10 (a primary species) and 19 (riverine) showed little penetration of disturbed biotopes in the pitfall analysis (where 9 was not represented).
The Kolmogorov–Smirnov one-sample goodness-of-fit test was carried out for each abundance distribution in Figs 3–5, for both log-series and log-normal models. As goodness-of-fit was applied to data with discrete distributions, corrections were not applied to the calculations (Daniel 1990). In the majority of cases, results were not significant (P > 0·2) and in only two cases did observed distributions vary significantly, both from the log-series distribution (primary B transect; Fig. 3d; Dmax = 0·3261, N = 18, P < 0·05) and the interior-primary association from the FIT at primary site A (Fig. 5c; Dmax= 0·5963, N = 8, P < 0·01).
The results of calculations for alpha for pitfall data were inconclusive (Table 3), although values were lowest in plantation forest. For other diversity measures, for both pitfall and FIT samples, diversity was lower in logged compared with interior-primary forest, with the lowest diversity again recorded from plantation forest (Table 3). Data showed that species richness in logged forest was generally higher than individual transects from primary interior-forest, closer to species richness in riverine forest (Table 3): the reasons for this will be discussed later. Species richness was lowest in plantation forest. From pitfall data, the mean number (± SE) of species was 41·67 ± 1·45 from primary interior forest, 45·67 ± 4·49 from logged forest, 48·0 ± 0·0 from riverine forest and 29·0 ± 4·0 from plantation forest (Table 3). The same trends were shown in the FIT data (Table 3). Evenness was lowest, and dominance highest, in riverine and logged forest (Table 3). In terms of beta-diversity (Tables 4 and 5), in both pitfall and FIT samples greatest faunal similarities were found between logged forest and riverine communities, between pitfall samples in plantation A and riverine forest (Table 4), and between similar habitats (Tables 4 and 5).
Table 3. Species richness, number of specimens, alpha diversity measurement, Shannon and Berger–Parker diversity indices for samples collected by two trapping methods from riverine, interior-primary, logged and plantation forest
| || || || ||Alpha||Shannon||Berger–Parker|
|Pitfall||Riverine A||48||3082|| 8·10||1·17||2·20||0·03||0·57||0·42|
| ||Riverine B||48||7410|| 6·87||0·99||1·64||0·02||0·42||0·66|
| ||Primary A||39||1648|| 7·17||1·15||2·63||0·03||0·72||0·26|
| ||Primary B||42||2253|| 7·33||1·13||2·75||0·02||0·74||0·15|
| ||Primary C||44||1755|| 8·19||1·24||2·51||0·03||0·66||0·31|
| ||Logged A||37||2695|| 6·07||1·00||1·74||0·03||0·48||0·61|
| ||Logged B||52||3081|| 8·89||1·23||1·62||0·03||0·41||0·66|
| ||Logged D||48||6721|| 6·99||1·01||1·41||0·02||0·36||0·72|
| ||Plantation A||33||3083|| 5·16||0·90||1·18||0·03||0·34||0·74|
| ||Plantation B||25|| 810|| 4·89||0·98||2·37||0·04||0·74||0·29|
| ||Primary A||38|| 214||13·43||2·18||3·20||0·06||0·88||0·11|
| ||Primary B||34|| 239||10·84||1·86||2·71||0·08||0·77||0·23|
| ||Logged D||42||1010|| 8·85||1·37||2·24||0·05||0·60||0·41|
| ||Plantation B||14|| 86|| 4·74||1·27||2·05||0·12||0·78||0·31|
Table 4. Similarities between samples collected by baited pitfall trap in riverine, interior-primary, logged and plantation forest, measured using the Sorensen index (CN). Index values greater than 0·5 are highlighted in bold
|Transect*||Riverine B||Primary A||Primary B||Primary C||Logged A||Logged B||Logged D||Plantation A||Plantation B|
Table 5. Similarities between flight intercept traps in riverine, interior-primary, logged and plantation forest, measured using the Sorensen index (CN). Index values greater than 0·5 are highlighted in bold
|FIT location*||Primary A||Primary B||Logged D||Plantation B|