Ground-Dwelling Ants, Termites, Other Macroarthropods And Earthworms
“Best bet” Land-use Systems
Thematic reports
Impact of different land uses on biodiversity
An Intensive Biodiversity Baseline Study in Jambi Province,Central Sumatra, Indonesia
Unique id: 7
Source file: D:\Projects\ASB\ASB Country and Thematic reports - xml\Above ground biodiversity assessmet WG\C-Sec6-7.xml
Authors: D.E. Bignell, E. Widodo, F.X. Susilo, H. Suryo
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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
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 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
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,
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---à BS3---à BS6---à BS8---àBS10 ---à BS12 ---à BS14
Table 7.1.
Seven landuses selected for ant and other macrofaunal sampling in Pasir Mayang and adjacent areas of central Sumatra.
|
Site coding |
Dominant vegetation
form |
General character |
GPS reference |
|
BS 1 |
Intact rainforest |
A small area of pristine lowland forest on a moderately steep slope, well drained with closed stratified canopy and generally light understorey. Tree buttresses and stilts present. |
01-04-47 S 102-06-02 E Pasir Mayang |
|
BS 3 |
Secondary rainforest |
A ridge-top site contiguous with BS1 but logged-over with secondary regrowth on old log collection points and skid trails. Transects and pitfalls placed to run through secondary areas. Generally patchy canopy but of limited stratification. High liana/creeper burden. |
01-04-43 S 102-05-55 E Pasir Mayang |
|
Site coding |
Dominant vegetation
form |
General character |
GPS reference |
|
BS 6 |
Young 3/4 yrs Paraserianthes plantation |
A heavily disturbed site with line planted Sengon trees established after complete clearance. Canopy very open and the ground with a heavy load of dead wood. |
01-05-59 S 102-06-43 E Pasir Mayang |
|
BS 8 |
Rubber plantation |
A mature monospecific plantation in current production for latex, located on a gentle 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. |
01-05-25 S 102-07-05 E Pasir Mayang |
|
BS 10 |
Jungle rubber |
Mixture of old rubber trees still in production and secondary forest regrowth with high liana/creeper burden. About 25-30 yrs. old, at end of cycle ready for felling. Canopy closure ± complete and well stratified. Flat site, riverine. |
01-10-12 S 102-06-50 E Pancuran Gading |
|
BS 12 |
Imperata cylindrica Grassland: "alang-alang" |
Large open ridge-top site devoid of trees with knee-high uniform stand of course grass. Little or no dead wood. Ground cracked and very hard. |
01-36-05 S 102-21-22 E. Kuamang Kuning |
|
BS 14 |
Cassava garden |
Open ridge-top site with line-planted Cassava, about 2 yrs old. Weeded to prevent growth of other vegetation. Ground very disturbed but little or no dead wood. |
01-35-58 S 102-21-11 E Kuamang Kuning |
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.)
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 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.
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:
5 sampling points (for monoliths) are located and marked within the transect.
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.
At each sampling point litter is removed from within a 25cm quadrat and hand-sorted at the site.
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 m2samples, i.e. the width of the block plus two trench widths, squared.
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.
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. 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
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 control site |
B |
C |
D |
E |
|
|
|
|
|
|
|
|
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?
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:
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).
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
(
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 sampled from monoliths. |
Total biomass of ants sampled 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) give average litter-ant
abundance across several woodland sites in
Table 7.5
Trends across the sites relatives to the control sites (BS1)
|
|
Landuse System |
||||||
|
Parameter |
Natural control site = BS1 |
BS1 |
BS6 |
BS8 |
BS10 |
BS12 |
BS14 |
|
|
|
|
|
|
|
|
|
|
Numerical density |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Arithmetical average of monoliths, nos m-2 |
352 |
522 |
550 |
134 |
541 |
80 |
48 |
|
p value for comparison with control site (transformed
data) |
0 |
ns. |
ns. |
ns. |
ns. |
ns. |
ns. |
|
% difference of means from control site |
0 |
+48% |
+56% |
-62% |
+54% |
-77% |
-86% |
|
|
|
|
|
|
|
|
|
|
Biomass density |
|
|
|
|
|
|
|
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