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Indicator groups and faunal richness

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Species richness is a popular indicator of ecosystem vitality, but is difficult to assess. Many natural resource managers seek an efficient bioindicator, but the link between candidate indicators and the richness of other taxononic groups remains elusive. A series of faunal surveys in the Mbalmayo Forest Reserve in Cameroon suggest that it may be possible to devise faunal bioindicators. The species richness of birds, of butterflies and of termites is significantly correlated with total faunal richness across eight species groups, suggesting that these groups may have potential as bioindicators, alone or in combination. Although expensive, further research is warranted because of the substantial potential benefits and implications of the use of indicator groups.
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INDICATOR GROUPS AND FAUNAL RICHNESS
JEROME K VANCLAY
School of Environmental Science and Management, Southern Cross University
PO Box 157, Lismore NSW 2480, Australia [JVanclay@scu.edu.au]
(Submitted, 25th June 2003; Accepted, 19th May 2004; Published, 3rd June 2004)
ABSTRACT. Species richness is a popular indicator of ecosystem vitality, but is difficult
to assess. Many natural resource managers seek an efficient bioindicator, but the link
between candidate indicators and the richness of other taxononic groups remains elusive. A
series of faunal surveys in the Mbalmayo Forest Reserve in Cameroon suggest that it may
be possible to devise faunal bioindicators. The species richness of birds, of butterflies and of
termites is significantly correlated with total faunal richness across eight species groups,
suggesting that these groups may have potential as bioindicators, alone or in combination.
Although expensive, further research is warranted because of the substantial potential
benefits and implications of the use of indicator groups.
Keywords: alpha diversity, species richness, bioindicator, surrogate, butterflies, termites.
1 INTRODUCTION
Natural resource managers need a “canary” to draw attention to sites of special significance and to
forewarn them of impending problems (cf. the coal miner’s canary to warn of fatal methane levels). It
is impractical to comprehensively monitor every aspect of a resource; efficiency demands the use of
indicators as proxies (or surrogates in the sense of Prendergast et al. 1993) for comprehensive
assessment. The choice of indicator is critical, not only because of the inferences that may be drawn
from it, but also because an efficient indicator may free funds from monitoring for more productive
research, maintenance of the resource, and education of its constituency.
Researchers have considered many potential indicators (Brown 1991) or surrogates (Oliver and
Beattie 1996), including plant genera (e.g., Prance 1994), vegetative morphology (e.g., Gillison et al.
1996), vegetative structure (e.g., Ferris-Kaan et al. 1998), sound patterns (e.g., Riede 1993), birds
(Garson et al. 2003), insects (e.g., Halffter and Favila 1993, Kremen 1994) and rare species (Lawler et
al. 2003). While morphology and structure-based assessments may eventually offer reliable and
automated monitoring, many researchers resort to faunal indicators, assuming that their inter-
relationships with other fauna and flora will also extend to species richness. The expectation is that
species richness within a particular (often conspicuous) group should be correlated with the overall
faunal richness (and presumably also with vegetative richness), and thus that the welfare of the selected
indicator group should offer an insight into the state of the system as a whole. Unfortunately, there is
little empirical evidence to support the contention (e.g., Lindenmayer 1999, Ricketts et al. 2002,
Vessby et al. 2002).
Lawton et al. (1998) examined species richness (or alpha diversity as defined by Whittaker 1977) in
several animal groups (birds, butterflies, beetles, ants, termites, nematodes) sampled in the Mbalmayo
Forest Reserve, Cameroon (11°E, 3°N, 650 m above sea level) during 1992-94, and suggested that
assessments of habitat change based on familiar groups (e.g., birds, butterflies) may mislead because of
low pair-wise correlations between groups and weak trends with disturbance. Their conclusion may be
unnecessarily pessimistic, because such indicators may not be used to infer the richness within other
groups, but rather to gain an insight into overall species richness. An alternative interpretation of their
data offers a more promising prognosis, and does not exclude the possibility that some species groups
may indeed indicate overall faunal richness.
Lawton et al. (1998) found that 40 of 45 between-group correlations did not differ significantly
from zero (i.e., P>0.05), and thus found no reason to reject the null hypothesis that the species richness
within any one group bore no relationship to the richness in any other group. This is not the question
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usually posed by resource managers, who often want to make inferences about the total species
richness. This question is explored below, using the Mbalmayo data kindly provided by Prof. John
Lawton.
2 DATA
The Mbalmayo data involve counts of individual species or morphospecies within nine taxonomic
groups sampled at six sites (Table 1). These observations have been adjusted to reflect an equal
sampling effort (Lawton et al. 1998), so some of the reported counts are fractional. Forty-five of the 54
possible site-species combinations were sampled, in most cases with a single sample, although two
samples were available in 15 instances. Nine site-species combinations remained unsampled. There are
two problems with these missing data: the column representing partial mechanical clearance, where
only 5 of the 9 species groups are sampled; and the row representing canopy ants, which were sampled
at only half of the sites. Despite this weakness, there are few better data presently available to address
this important and urgent question.
Table 1. Species counts from Mbalmayo Forest Reserve, Cameroon (Lawton et al. 1998).
Site and treatment †
Taxonomic group NP OS PCman PCmech CC FF
Birds 45 45 29 5 9
Butterflies 29, 33 51 30 28 30, 31 14
Malaise beetles ‡ 27, 31.5 40.5 33.5 32, 36.5 48.7
Intercept beetles ‡ 24, 47 113.5 41 59, 70.5 42.7
Canopy beetles 72 78 53, 80 91, 61 49, 46
Leaf litter ants 62, 55.3 73.6 79 72.8 46, 58.6
Termites 46 53 53 16 24
Soil nematodes 70.11 62.8 69.41 57.4 62.8, 67.36 54.05
Canopy ants 38.1, 28.8 35.7, 28.9 23.7, 31.8
NP = Near-Primary forest, OS = Old-growth Secondary forest, PCman = Partly Cleared (manually) with
Terminalia ivorensis plantation 10-15 m tall, PCmech = Partly Cleared (mechanically) with T. ivorensis 10-15 m
tall, CC = Completely Cleared and planted to T. ivorensis 1-2 m tall, FF = manually cleared Farm Fallow (Lawson
et al. 1998). Malaise beetles = flying beetles caught in malaise traps; Intercept beetles = beetles caught in flight-
interception traps.
There are three ways to deal with the incomplete column (Table 1) representing partial mechanical
clearance: to omit the entire column, to pool it with the column representing partial manual clearance,
or to try to infer the missing values. The first option (omit) discards scarce information, and the third
option (infer missing values) involves making difficult and uncertain inferences, so the second option
was adopted. The two treatments involving partial clearance by manual and mechanical means are
similar in nature and in species counts (paired t-test, t6=0.6, P=0.6), and were combined. The row
representing canopy ants was omitted from the calculation of species totals (Table 2), but was included
in the analysis of possible species indicators.
Table 2. Maximum number of species recorded within each group at each site.
Taxon NP OS PC CC FF
Birds 45 45 29 5 9
Butterflies 33 51 30 31 14
Malaise beetles 31.5 40.5 33.5 36.5 48.7
Intercept beetles 47 113.5 41 70.5 42.7
Canopy beetles 72 78 91 49 0 †
Litter ants 62 73.6 79 58.6 60.5‡
Termites 46 53 53 16 24
Nematodes 70.1 62.8 69.4 67.4 54.1
Species total * 406.6 517.4 425.9 334 253
† Assumed to be zero, since no canopy. ‡ Interpolated from termite counts.
* Excludes canopy ants.
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Multiple samples occur in 14 instances (after combining the two rows concerning partial clearing),
so there was more than one possible way to compute the total number of species for a site. For instance,
the total number of species at a site could be based on the average or the greatest of these multiple
observations of a species group at the site. In most cases, the difference between alternative
calculations was small, with the greatest discrepancy being canopy beetles on partially cleared sites
where the average (of 4 counts) was 71 and the maximum was 91 species. The present study based the
estimates of species totals on the greatest number of species observed, with the assumption that smaller
numbers were incomplete counts, and that the largest observations did not include vagrants (or
‘tourists’ in the sense of Moran and Southwood 1982). The possibility that the same beetle species may
occur in the malaise, flight interception and canopy data was dismissed, as these different trapping
methods catch different components of the beetle fauna (Lawton et al. 1998).
Two cells in the Table 2, canopy beetles and litter ants, were not sampled on the farm fallow site
and some assumptions were required to complete the table. Table 2 follows Lawton et al. (1998) in
assuming that no canopy beetles would be detected in farm fallow since no canopy was present at this
site. The number of litter ants was estimated through regression. Because of their similar niche and
reasonable correlation (r3=0.83, P=0.07), the likely number of litter ant species was estimated using a
simple linear regression of litter ants on termites (nants=50.2+0.42ntermites). These two inferred values
were used only to estimate the total number of species present on each site, and were not used directly
in further regression analyses.
3 ANALYSIS
The relationship between within-group richness and total faunal richness was examined using
regression analyses and permutation tests. Regression analyses were used to seek a predictor set of
organisms such that changes in the biological status of the predictor set reflect similar changes in a
wider group of organisms (Kitching 1993). Evidence of such qualities may be inferred from the
relationship Ni=ni + e, where e is a random error, ni is the number of species within group i, Ni is the
“extra-group” richness, the number of species in other groups Nij≠i nj, such that total surveyed
species richness is N=Ni+ni for anyi. Using total richness N as the response variable would artificially
enhance the quality of the fit (e.g., since N = ni+Ni = ni +e' even when no relationship between ni and
Ni exists). For the 14 site-species combinations where multiple samples were available, the individual
samples were used in further analyses, providing a total of 60 data observations (including the
observations on canopy ants, and excluding the presumed values for canopy beetles, canopy ants and
leaf litter ants in the farm fallow, see Appendix).
Figure 1. Extra-group richness (Ni) versus within-group richness (ni) in Mbalmayo
Forest Reserve, Cameroon. The solid line is the relationship Ni=246+3.5ni and the
dotted line represents the mean of the unfilled symbols, 342.
100
200
300
400
500
0 40 80 120
Within-group richness
Extra-group richness .
Birds Butterflies
Malaise beetles Intercept beetles
Canopy beetles Litter ants
Termites Nematodes
Canopy ants
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Preliminary graphical analyses of these data (Figure 1) reveal that
1. the five sample sites are evident in these data as five bands, declining with slope -1 as within-group
richness increases (i.e., the relationship extra-group richness Ni = site richness Nsite minus within-
group species count ni),
2. some species groups (birds, butterflies and termites, illustrated with filled symbols) exhibit a
correlation between extra-group and within-group species numbers, and that
3. for some species (e.g., beetles caught in flight interception traps), the number of extra- and within-
group species appears uncorrelated.
Figure 2. Four species groups exhibit both large intercept and steep slope. Bars
indicate one standard error and illustrate the significance of the estimated slope.
Butterflies
Birds
Termites
Canopy ants
0
2
4
6
0 100 200 300
Intercept
Slope
These trends were confirmed by preliminary statistical analyses, which revealed three taxonomic
groups of interest (Table 3 and Figure 2): the birds, butterflies and termites, each of which has
relatively steep slope (β1), large intercept (β0), high correlation with extra-group richness (r), and a low
probability (P) that this is due to chance. Canopy ants exhibit a trend similar to these three groups
(Figure 2), but were recorded only at two sites (partially and completely cleared), so estimates for this
group are not significant (Table 3).
Table 3. Correlation coefficients for each species group.
Taxon n Sites β1 β0 r P
P
ˆ
Birds 5 5 3.4 271 0.76 0.06 0.06
Butterflies 8 5 5.8 180 0.81 0.006 0.006
Malaise beetles 7 5 -5.3 535 -0.43 0.16 0.17
Intercept beetles
7 5 0.4 303 0.15 0.37 0.40
Canopy beetles 8 4 1.3 257 0.44 0.13 0.15
Litter ants 7 4 2.9 158 0.62 0.06 0.07
Termites 5 5 3.8 203 0.77 0.05 0.07
Nematodes 7 5 4.1 61 0.3 0.25 0.23
Canopy ants 6 2 4.6 251 0.51 0.14 0.15
P is the probability derived from the correlation coefficient;
P
ˆis the estimated probability derived from a permutation test.
Tests of this kind may indicate a significant result purely due to chance. If the observed correlations
are due to chance alone, the associated probabilities should be uniformly distributed on [0,1]. However,
in this case, the probabilities observed remain small, suggesting that the correlation between within-
and extra-group richness is real (Figure 3, where the slope of the observed probabilities is 0.29,
significantly different from 1.0, P<0.001).
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Figure 3. Probabilities reported in Table 3 are not distributed uniformly on [0,1].
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Cumulative frequency
P
A further way to confirm the robustness of these findings is to resample (Good 2000). The
permutation test (
ˆ) reported in Table 3 results from shuffling the within- and between-group richness
data (Appendix) 1000 times, and reporting the relative number of times that the observed correlation
could have arisen by chance. The estimates from this test correspond closely to the conventional
probability estimates obtained from the single-sided t-test (Cohen 1977). Results of these tests are
illustrated in Figure 4. The curves indicate the correlations (in decreasing order) observed in the
shuffled data; different curves arise because of different numbers of observations, numbers of sites, and
random sequences for each species. There are only six instances (out of 1000) in the shuffled data that
exhibit a correlation higher than that observed in the real data (0.81), so it is unlikely that this
correlation is due to chance. In contrast, there are 397 instances in the shuffled data that exhibit a
correlation higher than that observed for intercept beetles, illustrating that this correlation may simply
be a chance occurrence.
Figure 4. Results of permutation tests
-1
0
1
0 1000
Number of instances
Correlation
Birds
Butterflies
Malaise beetles
Intercept beetles
Canopy beetles
Litter ants
Termites
Nematodes
Canopy ants
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Regression analyses confirmed that it was appropriate to divide the data into two categories
(evidence for two categories F2,56=5.78, P=0.005; no evidence for additional categories F14,42=0.71,
P=0.8). One category contained four species groups, the birds, butterflies, termites, and canopy ants,
each of which exhibited a relationship with a slope of about 3.5 (evidence for positive slope in pooled
data: r=0.8, t22=3.5, P=0.0009). A simple linear regression (i.e., Ni0+β1ni) appeared adequate: a Box-
Cox analysis (Box and Cox, 1964, 1982) revealed no need to transform the response variable, and an
analysis of variance using six categories offered no evidence of a curvilinear relationship (F4,20=0.18,
P=0.9). The canopy ants were recorded on only two sites (PC and CC), so contribute little to the trend,
but lead to smaller residuals when included within the bird-butterfly-termite group than with the
remaining groups. The remaining category with five species groups exhibited no detectable trend (no
evidence for non-zero slope t34=0.93, P=0.2; and no evidence for a curvilinear relationship F2,34=1.05,
P=0.4). The resulting parameter estimates are given in Table 4.
Table 4. Parameter estimates to predict extra-group species richness from
within-group richness, Ni0+β1ni. Standard errors are shown in parentheses. All
parameters significant at P<0.01.
Species group Observations Intercept (β0) Slope (β1)
Birds, butterflies, termites,
& canopy ants
24 246.4
(27.6)
3.53
(0.81)
Other species groups 36 341.9
(11.9)
_
Butterflies and termites in
conjunction (Fig. 2)
5 133.8
(21.2)
2.20
(0.29)
Ideally, an indicator should have a high intercept (large and positive, because the “canary” should
die before the miners) and a steep slope (rich information content). However, the present data offer no
empirical way to discriminate between birds, butterflies and termites, as specific estimates of slope and
intercept for these groups do not differ significantly.
Figure 5. Extra-group richness (numbers of bird, beetle, litter ant and nematode species)
versus the number of butterfly plus termite species. The line represents the linear regression
Ni=133.8+2.2nI, where ni is the sum of butterfly and termite richness.
100
200
300
400
20 40 60 80 100
Richness of butterflies and termites
Extra-group richness
A “shopping basket” of selected surrogate taxa may form a better predictor set than a single species
group (e.g., di Castri et al. 1992, Kremen 1994, Oliver and Beattie 1996). Whilst the present data set is
too small to adequately resolve this issue, there is some evidence to support this contention and to draw
attention to the need for further research. For instance, butterflies and termites in conjunction provide a
good estimate of extra-group species richness (Table 4 and Figure 5). These estimates are based on the
regression of extra-group richness (total species minus the number of butterfly and termite species)
versus the numbers of termites plus the average of the numbers of butterfly counts reported in Table 1.
In this instance, there is a strong probability that the slope differs from zero (t3=7.59, P=0.002), even
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after allowing a Bonferroni adjustment (Neyman and Pearson 1928, Stewart-Oaten 1995) for the three
possible pairwise combinations of birds, butterflies and termites (P=0.007), or the 36 possibilities of
pairing any of the groups (P=0.08). A permutation test that shuffled the data 10,000 times indicated an
estimated probability of 0.003.
4 DISCUSSION
The results reported in Table 3 and 4 obviously depend upon several assumptions, e.g., those
involved in:
1. collapsing Table 1 (i.e., assuming partial clearance by manual and mechanical means are not
significantly different),
2. completing missing cells in Table 2 (i.e., assuming no canopy beetles where there is no canopy, and
predicting richness of litter ants from termite richness),
3. estimating total surveyed faunal richness (i.e., using maximum rather than the average richness in
cells with multiple samples, omitting canopy ants from the total, assuming no beetle species occurs
in both interception and malaise traps), and
4. assuming that the faunal richness across the eight groups surveyed is indicative of the total richness
of all fauna (including fauna not sampled in the Mbalmayo study).
Fortunately, the findings appear to be relatively robust and hold when the first three of these
assumptions are varied, at least for the three groups involving birds, butterflies, and termites. When the
data were processed in other ways, consistent results were obtained for birds, butterflies and termites,
but canopy ants seemed more closely aligned with the second category of organisms under some
assumptions.
6 CONCLUSION
These findings support the contention (Garson et al. 2002) that some species groups (e.g., birds,
butterflies, and termites, in the case of Mbalmayo) may be useful indicators of the overall species
number. It seems that an even better indication of overall faunal richness may be obtained by using
diverse groups in conjunction (e.g., butterflies plus termites).
This observation must be qualified since the findings of the present study depend on the validity of
four assumptions made during the analyses (see above), and do not take into account the nature of these
species (viz. exotic versus endemic). It seems possible to make inferences about total species richness,
but one should not assume that all faunal groups follow the response of the chosen group (cf. Lawton et
al. 1998). These findings are specific to disturbed forest near Mbalmayo Forest Reserve in Cameroon,
and it should not be assumed that they are generally applicable. It seems feasible that butterflies and
termites in combination may be good indicators for other humid forest environments, but it is possible
that other species groups may be more effective indicators in other regions (e.g., arid or temperate
regions).
Faunal richness may not be a good indicator of vegetative disturbance, as some disturbances (e.g.,
partial clearance) may actually increase species richness (Table 2, and Lawton et al. 1998), and
disturbance can be gauged more easily and reliably in other ways (Watt 1998).
Because complete faunal inventories are difficult, time-consuming and expensive (Lawton 1998,
Stork 1995), most natural resource managers cannot monitor the status of all species operationally.
Many managers and researchers yearn for practical indicators that can be monitored efficiently and
extrapolated reliably. Several surrogates have been suggested, but little empirical evidence has been
tendered in support of these nominations. The present study offers some empirical evidence to support
the notion that selected species groups may serve as indicators of a broader group of fauna, particularly
when used in conjunction. If so, managers and researchers may be better served by reliable,
comprehensive studies of selected groups, rather than superficial attempts to survey the whole fauna.
However, the issue warrants further research (notably comprehensive faunal surveys for a range of
sites) since the potential benefits and implications are considerable.
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APPENDIX 1. DATA USED IN ANALYSES
Datum Site Group Within-group richness Extra-group richness
1 NP Birds 45 361.61
2 OS Birds 45 472.4
3 PCman Birds 29 396.91
4 CC Birds 5 328.96
5 FF Birds 9 243.98
6 NP Butterflies 29 377.61
7 NP Butterflies 33 373.61
8 OS Butterflies 51 466.4
9 PCman Butterflies 30 395.91
10 PCmech Butterflies 28 397.91
11 CC Butterflies 30 303.96
12 CC Butterflies 31 302.96
13 FF Butterflies 14 238.98
14 NP Malaise beetles 27 379.61
15 NP Malaise beetles 31.5 375.11
16 OS Malaise beetles 40.5 476.9
17 PCman Malaise beetles 33.5 392.41
18 CC Malaise beetles 32 301.96
19 CC Malaise beetles 36.5 297.46
20 FF Malaise beetles 48.7 204.28
21 NP Intercept beetles 24 382.61
22 NP Intercept beetles 47 359.61
23 OS Intercept beetles 113.5 403.9
24 PCman Intercept beetles 41 384.91
25 CC Intercept beetles 59 274.96
26 CC Intercept beetles 70.5 263.46
27 FF Intercept beetles 42.7 210.28
28 NP Canopy beetles 72 334.61
29 OS Canopy beetles 78 439.4
30 PCman Canopy beetles 53 372.91
31 PCman Canopy beetles 80 345.91
32 PCmech Canopy beetles 91 334.91
33 PCmech Canopy beetles 61 364.91
34 CC Canopy beetles 49 284.96
35 CC Canopy beetles 46 287.96
36 NP Litter ants 62 344.61
37 NP Litter ants 55.3 351.31
38 OS Litter ants 73.6 443.8
39 PCman Litter ants 79 346.91
40 PCmech Litter ants 72.8 353.11
41 CC Litter ants 46 287.96
42 CC Litter ants 58.6 275.36
43 NP Termites 46 360.61
44 OS Termites 53 464.4
45 PCman Termites 53 372.91
46 CC Termites 16 317.96
47 FF Termites 24 228.98
48 NP Nematodes 70.11 336.5
49 OS Nematodes 62.8 454.6
50 PCman Nematodes 69.41 356.5
51 PCmech Nematodes 57.4 368.51
52 CC Nematodes 62.8 271.16
53 CC Nematodes 67.36 266.6
54 FF Nematodes 54.05 198.93
55 PCman Canopy ants 38.1 425.91
56 PCman Canopy ants 28.8 425.91
57 PCmech Canopy ants 35.7 425.91
58 PCmech Canopy ants 28.9 425.91
59 CC Canopy ants 23.7 333.96
60 CC Canopy ants 31.8 333.96
... An organism that can give respond (Weissman et al. 2006), indication (McGeoch 1998), early warning Dale and Bayeler 2001), or representation (Hilty and Merenlender 2000;Vanclay 2004), reflection (Noss 1990;Vanclay 2004), and information (McGeoch 1998) and also evaluation (Burger and Gochfeld 2001;Carignan and Villard 2002) of the condition and/ or changes that occur in an ecosystem called bioindicator. Bioindicator is an important component in ecosystem management and biodiversity conservation (Andersen 1999). ...
... An organism that can give respond (Weissman et al. 2006), indication (McGeoch 1998), early warning Dale and Bayeler 2001), or representation (Hilty and Merenlender 2000;Vanclay 2004), reflection (Noss 1990;Vanclay 2004), and information (McGeoch 1998) and also evaluation (Burger and Gochfeld 2001;Carignan and Villard 2002) of the condition and/ or changes that occur in an ecosystem called bioindicator. Bioindicator is an important component in ecosystem management and biodiversity conservation (Andersen 1999). ...
... Termites are one of the main decomposer in tropical terrestrial ecosystems , and ecosystem engineers through their activities which help improve soil structure and nutrient cycling (Jones et al. 1994: Levelle et al. 1997). In addition, termite species richness showed a high correlation to the diversity of other taxon groups in the same habitat (Vanclay 2004), and the complexity of vascular plants . Termites also showed high sensitivity to environmental conditions, both biotic or abiotic that exposed them, as well as on ecosystem processes . ...
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Pribadi T,Raffiudin R,HarahapIS (2011)Termites community as environmental bioindicators in highlands: a case study in eastern slopes of Mount Slamet, Central Java. Biodiversitas 12: 235-240. Termites ecological behaviour is much affected by land use change and disturbance level. Their variation in diversity can be used as bioindicator of environmental quality. However, termite community response to land use changes and habitat disturbance in highland ecosystems remains poorly understood. This study was conducted to investigate the response of termite community to land use intensification and to explore their role as environmental bioindicator in Mount Slamet. A standard survey protocol was used to collect termites in five land use typesof various disturbance levels,i.e. protected forest, recreation forest, production forest,agroforestry, and urban area. It was found two termite families i.e. Rhinotermitidae and Termitidae with seven species, i.e Schedorhinotermes javanicus, Procapritermes sp, Pericapritermes semarangi, Macrotermes gilvus, Microtermes insperatus, Nasutitermes javanicus, and N. matanganensis. Termite species' richness and evenness, Shannon-Wiener index, relative abundance, and biomass of termite were declined along with the land use types and disturbance level from protected forest to urban area. Habitat disturbance was the main declining factor of termite diversity. Termite composition changed along with the land use disturbance level. Soil feeding termites were sensitive to the disturbance – they were not found in urban area. Hence, their presence or absence can be used as environmental bioindicator to detect habitat disturbance.
... An organism that can give respond (Weissman et al. 2006), indication (McGeoch 1998), early warning Dale and Bayeler 2001), or representation (Hilty and Merenlender 2000;Vanclay 2004), reflection (Noss 1990;Vanclay 2004), and information (McGeoch 1998) and also evaluation (Burger and Gochfeld 2001;Carignan and Villard 2002) of the condition and/ or changes that occur in an ecosystem called bioindicator. Bioindicator is an important component in ecosystem management and biodiversity conservation (Andersen 1999). ...
... An organism that can give respond (Weissman et al. 2006), indication (McGeoch 1998), early warning Dale and Bayeler 2001), or representation (Hilty and Merenlender 2000;Vanclay 2004), reflection (Noss 1990;Vanclay 2004), and information (McGeoch 1998) and also evaluation (Burger and Gochfeld 2001;Carignan and Villard 2002) of the condition and/ or changes that occur in an ecosystem called bioindicator. Bioindicator is an important component in ecosystem management and biodiversity conservation (Andersen 1999). ...
... Termites are one of the main decomposer in tropical terrestrial ecosystems , and ecosystem engineers through their activities which help improve soil structure and nutrient cycling (Jones et al. 1994: Levelle et al. 1997). In addition, termite species richness showed a high correlation to the diversity of other taxon groups in the same habitat (Vanclay 2004), and the complexity of vascular plants . Termites also showed high sensitivity to environmental conditions, both biotic or abiotic that exposed them, as well as on ecosystem processes . ...
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Full-text available
Pribadi T,Raffiudin R,HarahapIS (2011)Termites community as environmental bioindicators in highlands: a case study in eastern slopes of Mount Slamet, Central Java. Biodiversitas 12: 235-240. Termites ecological behaviour is much affected by land use change and disturbance level. Their variation in diversity can be used as bioindicator of environmental quality. However, termite community response to land use changes and habitat disturbance in highland ecosystems remains poorly understood. This study was conducted to investigate the response of termite community to land use intensification and to explore their role as environmental bioindicator in Mount Slamet. A standard survey protocol was used to collect termites in five land use typesof various disturbance levels,i.e. protected forest, recreation forest, production forest,agroforestry, and urban area. It was found two termite families i.e. Rhinotermitidae and Termitidae with seven species, i.e Schedorhinotermes javanicus, Procapritermes sp, Pericapritermes semarangi, Macrotermes gilvus, Microtermes insperatus, Nasutitermes javanicus, and N. matanganensis. Termite species' richness and evenness, Shannon-Wiener index, relative abundance, and biomass of termite were declined along with the land use types and disturbance level from protected forest to urban area. Habitat disturbance was the main declining factor of termite diversity. Termite composition changed along with the land use disturbance level. Soil feeding termites were sensitive to the disturbance – they were not found in urban area. Hence, their presence or absence can be used as environmental bioindicator to detect habitat disturbance.
... More recently biodiversity indicators were simply defined as 'species with occurrence patterns that are correlated with the species richness of a larger group of organisms' (Mac Nally & Fleishman, 2004). Recent work demonstrated that also higher taxa may be good surrogates of species richness (Báldi, 2003), and some taxonomic groups above species level appear to adequately serve the role of biodiversity indicators (Ricketts, Daily & Ehrlich, 2002;Vessby et al., 2002;Vanclaj, 2004). ...
... According to Vanclaj (2004), these conclusions may be unnecessarily pessimistic, because such indicators may not be necessarily used to infer species richness within other groups, but rather to extrapolate information on overall species richness. Moreover, Mac Nally & Fleishman (2002, 2004 and Fleishman et al. (2005) pointed out that it is unlikely that indicator species from a single taxonomic group will provide information on species richness of the entire biota at spatial scales meaningful for most land-use decisions, suggesting the use of combinations of indicator species. ...
... According to Vanclaj (2004), these conclusions may be unnecessarily pessimistic, because such indicators may not be necessarily used to infer species richness within other groups, but rather to extrapolate information on overall species richness. Moreover, Mac Nally & Fleishman (2002, 2004 and Fleishman et al. (2005) pointed out that it is unlikely that indicator species from a single taxonomic group will provide information on species richness of the entire biota at spatial scales meaningful for most land-use decisions, suggesting the use of combinations of indicator species. Mac Nally & Fleishman (2004) argued that prediction of species richness should be regarded as a testable hypothesis in the form of a statistical model, i.e. a function of the occurrence of indicator species. ...
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1. Estimates of species richness obtained from exhaustive field inventories over large spatial scales are expensive and time-consuming. For this reason, efficiency demands the use of indicators as ‘surrogates’ of species richness. Biodiversity indicators are defined herein as a limited suite of taxonomic groups the species richness of which is correlated with the species richness of all other taxonomic groups present in the survey area. 2. Species richness in ground water was assessed at different spatial scales using data collected from six regions in Europe. In total, 375 stygobiotic species were recorded across 1157 sites and 96 aquifers. The taxonomic groups collected from more than one site and with more than two species (Oligochaeta, Gastropoda, Cyclopoida, Harpacticoida, Ostracoda, Isopoda, Amphipoda, Bathynellacea and Acari) were used to develop nonparametric models to predict stygobiotic biodiversity at the aquifer scale. 3. Pair-wise correlations between taxonomic groups were low, i.e. variation in species richness of a single taxonomic group did not usually reflect variation of the other groups. In contrast, multiple regressions calculated between species richness of any combination of taxa and extra-group species richness along the six regions resulted in a number of significant relationships. 4. These results suggest that some taxonomic groups (mainly Copepoda and Amphipoda and, to a lesser extent, Oligochaeta and Gastropoda) combined in different ways across the regions, were good biodiversity indicators in European groundwater ecosystems. However, the uneven distribution of taxonomic groups prevented selection of a common set of indicators for all six regions. Faunal differences among regions are presumably related to both historical and ecological factors, including palaeogeography, palaeoecology, geology, aquifer fragmentation and isolation, and, less clearly, anthropogenic disturbance.
... Komponen ekosistem yang tidak normal berdampak pada perubahan mekanisme kerja pada suatu organisme. Beberapa organisme mampu memberikan tanggapan (Weissman et al. 2006), pertanda (Elliot 1997), peringatan dini (Jones & Eggleton 2000), atau representasi (Hilty & Merylender 2000;Vanclay 2004) serta refleksi (Vogt et al. 1997;Didden 2003;Vanclay 2004 Ketika ciri-ciri biologis dijadikan sebagai indikator, maka parameter biologis adalah pemicunya (Straalen 1997 (Jones & Eggleton 2000). ...
... Komponen ekosistem yang tidak normal berdampak pada perubahan mekanisme kerja pada suatu organisme. Beberapa organisme mampu memberikan tanggapan (Weissman et al. 2006), pertanda (Elliot 1997), peringatan dini (Jones & Eggleton 2000), atau representasi (Hilty & Merylender 2000;Vanclay 2004) serta refleksi (Vogt et al. 1997;Didden 2003;Vanclay 2004 Ketika ciri-ciri biologis dijadikan sebagai indikator, maka parameter biologis adalah pemicunya (Straalen 1997 (Jones & Eggleton 2000). ...
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Ecosystem alterations not only affect habitat conditions but also have an impact on biotic components. The presence of organisms provides a response of habitat alteration can be used as indication organism. Indication organisms or bioindicator are key components in ecosystem management. This paper aims to evaluate and review the role of termites as bioindicator. Bioindicator defined as organisms or group of organism reflect and inform the ecosystem circumstance; environmental, ecological and biodiversity status as well as. Main criteria of bioindicator are as follow: taxonomical and biological characters of these organisms well-knew, cosmopolitan organisms, they have a well-response to habitat alteration, and their responses are closed correlated to all communities or properties of stress factors. Termites showed responses to environmental change, especially in habitat alteration. Termites responded to habitat alteration on termites composition change and termites richness decrease. Furthermore, termite�s richness strong correlated with another taxon in their community. Biological and taxonomical termites are well-known. In addition, a standard survey of termites has been developed to explore termite�s richness in tropics ecosystems. This implication, termites can be applied as one of the indicator organisms or bioindicator, notably their relation in ecological indicator and biodiversity indicator.
... Their value as ecological indicator is still to be proven, but they possess the attributes to be good bioindicators: widespread geographic distribution, high abundance, taxonomic and ecological diversity, low locomotor capacity, functional importance, short time response to disturbance, sensitivity to environmental conditions, easy sampling and identification [106]. Moreover, their richness can be correlated with the diversity of other groups, being useful indicators of the overall species number [107]. Some authors observed that termite species richness, relative abundance and composition are affected by habitat disturbance and fragmentation, but also by land use [108][109][110]. ...
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The dramatic increase in soil degradation in the last few decades has led to the need to identify methods to define not only soil quality but also, in a holistic approach, soil health. In the past twenty years, indices based on living communities have been proposed alongside the already proven physical-chemical methods. Among them, some soil invertebrates have been included in monitoring programs as bioindicators of soil quality. Being an important portion of soil fauna, soil arthropods are involved in many soil processes such as organic matter decomposition and translocation, nutrient cycling, microflora activity regulation and bioturbation. Many studies have reported the use of soil arthropods to define soil quality; among taxa, some have been explored more in depth, typically Acari and Collembola, while generally less abundant groups, such as Palpigradi or Embioptera, have not been investigated much. This paper aims to evaluate and compare the use of different soil microarthropod taxa in soil degradation/quality studies to highlight which groups are the most reported for soil monitoring and which are the most sensitive to soil degradation. We have decided not to include the two most present and abundant taxa, Acari and Collembola, in this paper in consideration of the vast amount of existing literature and focus the discussion on the other microarthropod groups. We reported some studies for each taxon highlighting the use of the group as soil quality indicator. A brief section reporting some indices based on soil microarthropods is proposed at the end of this specific discussion. This paper can be considered as a reference point in the use of soil arthropods to estimate soil quality and health.
... Registering the entire biological diversity at regional scale is an impossible task, whatever economic and time resources are available, since biological diversity is constantly changing. The use of surrogate taxa as biodiversity indicators of a specific area has been proposed as a way of addressing this problem and facilitating biodiversity management and conservation policies (WILLIAMS et al., 1997;VANCLAY, 2004). Spiders are often used as biodiversity indicators for the following reasons (MARC et al., 1999;PLATNICK, 1999;MAELFAIT et al., 2004;PEARCE & VENIER, 2006): they show high species richness, play an important role in terrestrial ecological webs, are of economic interest, are easy and inexpensive to sample (yielding quantitative analyzable results), their taxonomy is reasonably well known in developed countries, and their communities are sensitive to environmental change. ...
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The aims of this research are to determine the time of year with the highest species richness to avoid whole-year samplings in future studies, and to contribute to the seasonal dynamics and faunistic knowledge of the spider fauna of the study area. Hence, it summarizes temporal changes in species richness and seasonal activities of spiders collected in several temperate forests sampled using different methods. Results show the May-June transition as the period with the peak in species richness. However, this maximum takes place earlier in the epigeal stratum (May), and later on tree trunks (June). Additionally, lower strata show higher representation of species of long mating periods. Three species, Centromerita bicolor, Micrargus aper-tus and Midia midas are new records for Iberian fauna, while Peponocranium ludi-crum is a new genus record for Spain. Seasonal activities of Nemesia simoni and Labulla flahaulti are described for the first time. Data obtained in two of the sampled forests will make it possible to evaluate the efficiency of future short-term collecting protocols for the study area. • KEY WORDS: Araneae, seasonal activity, temperate forests, species richness, Spain. RESUMEN Los objetivos del presente trabajo son determinar la época del año que registra el mayor número de especies a fin de evitar muestreos de ciclos anuales completos en estudios futuros y contribuir al conocimiento faunístico y de las dinámicas estacio-nales de la araneofauna del área de estudio. Para ello, se compendia las variaciones temporales de la riqueza específica y de las actividades estacionales de las arañas capturadas en varios bosques templados muestreados a través de distintos métodos. Los resultados determinan la transición entre Mayo y Junio como el periodo que pre-senta el máximo de riqueza específica. Sin embargo, este máximo tiene lugar antes en el estrato epigeo (Mayo) y más tarde (Junio) en los troncos de los árboles. Además, los estratos más bajos muestran una mayor representación de especies de periodos de reproducción largos. 3 nuevas especies, Centromerita bicolor, Micrargus apertus y Midia midas, se citan para la Península Ibérica y un nuevo género, Peponocranium ludicrum, para España. Las actividades estacionales de
... Butterfly species or communities are well known indicators of different ecological factors since they usually react quickly to negative changes, but their flying ability also allows them to return if conditions become favourable (Lawton et al. 1998). Fruit-feeding butterfly communities are frequently used to measure these factors including their reaction to edgeeffect, stratification, fragmentation or habitat loss (Fermon et al. 2000(Fermon et al. , 2003Vanclay 2004;Bobo et al. 2006;Bossart et al. 2006;Bossart and Opuni-Frimpong 2009;Elbers and Bossart 2009). In this paper analysis of quantified samples collected in forests at different stages of degradationregeneration (from clear-cut to primary forest through abandoned farmland, young planted indigenous forest and middle-aged secondary growth) reveals evidence that rainforest associated fruit-feeding butterfly communities are able to recover. ...
Article
Successful regeneration of secondary tropical forest might be crucial in the conservation of rainforests, since large areas of primary forest have been destroyed or degraded. Animal communities might play an important role in restoration of biological diversity in these secondary habitats, since some groups have high mobility and capacity for dispersal. Fruit-feeding butterflies were trapped to measure differences between butterfly communities in primary rainforest and disturbed forest habitats of different stage of regeneration including clear-cut, abandoned farmland, newly planted forest and middle-aged secondary growth. 3465 specimens representing 114 species were identified from 56 traps operated for 36days. Extremely high values of rarefied species richness were estimated in the clear-cut habitat, due to the high number of singletons and doubletons. This was caused by a gap-effect that allowed penetration of canopy and open area species after disturbance. The differences between butterfly communities were best demonstrated through ecological composition, richness and abundance of indicator groups and habitat similarity based on Jaccard’s similarity index. The results show a clear ability of butterfly communities in degraded forest habitats to regenerate in 50–60years after clearance. KeywordsButterfly community recovery–Fruit-feeding butterflies–Rainforest regeneration–Kakum forest–Ghana
... Ants have been used to assess pollutant concentrations in Boreal forests (Maavara et al., 1994) and in Australia they are currently used to monitor disturbed ecosystems (Andersen et al., 1998(Andersen et al., , 2004Andersen and Mueller, 2000). There are also some reports of the possible use of termites as bioindicators (Masse et al., 2002;Vanclay, 2004), but only one paper deals with the possible use of social wasps as trace metal accumulators (Kowalczyk and Watala, 1989). In this paper, we report the possibility of using the common paper wasps of the genus Polistes in biomonitoring. ...
Article
The wasps of the genus Polistes (paper wasps), have a worldwide distribution and are widespread in human-built areas. Like other social wasps, they are at the top of food chains and are therefore exposed to the dangers of biomagnification, given that the larvae are fed predominantly with prey that consist of herbivorous insects. The larval faeces, larval fecal masses, in the form of a semi-solid ball, are made up of the residues of the diet of the larva, which are emitted and compressed on the floor of the cell during the larval metamorphosis. Larval fecal masses may accumulate lead (up to 36 times with respect to the adult body), therefore they were used as substrate for the analysis. From the analysis of sample nests of Polistes dominulus in various sites of the urban area of Florence, it emerges that the larval fecal masses are an analytical substrate with which it is possible to distinguish zones with differing degrees of lead pollution. The lead concentration measured in the larval fecal masses turns out to be directly correlated with vehicle traffic density, the main lead source in Florence when the survey was carried out. The notable increase in the lead concentration of larval fecal masses from the rural to the urban nest (11.15 times), in contrast with the much more limited level of pupae (4.39 times), seems to indicate the efficiency of the excretion and/or barrier mechanisms. These wasps seem to be a promising species for biomonitoring lead pollution in order to better understand its dynamics in anthropic ecosystems after the progressive diffusion of unleaded gasoline.
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Statistical analyses are based on a mixture of mathematical theorems and judgments based on subject matter knowledge, intuition, and the goals of the investigator. Review articles and textbooks, aiming for brevity and simplicity, sometimes blur and difference between mathematics and judgments. A folklore can develop, where judgments based on opinions become laws of what @'should@' be done. This can intimidate authors and readers, waste their time, and sometimes lead to analyses that obscure the information in the data rather than clarify it. Three familiar examples are discusses: the choice between Normal based and non-parametric methods, the use of multiple-comparison procedures, and the choice of sums of squares for main effects in unbalance ANOVA. In each case, commonly obeyed rules are shown to be judgments with which it is reasonable to disagree. A greater stress on models selection, aided by informal methods, such as plots, and by informal use of formal methods, such as tests, is advocated.
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Despite concern about the effects of tropical forest disturbance and clearance on biodiversity,, data on impacts, particularly on invertebrates, remain scarce. Here we report a taxonomically diverse inventory on the impacts of tropical forest modification at one locality. We examined a gradient from near-primary, through old-growth secondary and plantation forests to complete clearance, for eight animal groups (birds, butterflies, flying beetles, canopy beetles, canopy ants, leaf-litter ants, termites and soil nematodes) in the Mbalmayo Forest Reserve, south-central Cameroon. Although species richness generally declined with increasing disturbance, no one group serves as a good indicator taxon for changes in the species richness of other groups. Species replacement from site to site (turnover) along the gradient also differs between taxonomic groups. The proportion of `morphospecies' that cannot be assigned to named species and the number of `scientist-hours' required to process samples both increase dramatically for smaller-bodied taxa. Data from these eight groups indicate the huge scale of the biological effort required to provide inventories of tropical diversity, and to measure the impacts of tropical forest modification and clearance.
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(1) Comparative knockdown samples of the invertebrate fauna in trees were taken during summer in South Africa and in Britain. Betula pendula, Quercus robur, Robinia pseudoacacia, Buddleia and Salix species were sampled in both countries; Erythrina caffra was sampled in South Africa only. (2) A total of 41 844 individuals were sorted and identified; only two of these were molluscs, the rest arthropods. (3) The composition of the arboreal guilds in analysed with respect to the numbers and proportions of families, species and individuals, and by biomass. (4) Taxonomic similarities in the arthropod faunas of trees are compared within and across countries. (5) The narrow-leaved flexible-stemmed, willows (Salix species) had a depauparate fauna and proportionately more phytophagous species than the broad-leaved trees sampled. (6) There was a striking uniformity in the proportion of predatory species recorded on all trees in both countries, and also a constant proportion of phytophagous species on different broad-leaved trees. The relative numbers of chewing and sap-sucking species were highly variable, but when summed resulted in proportionally uniform numbers of phytophagous species. These phenomena are discussed in the light of recent debate on patterns in community structure.
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Biotic inventories provide critical data for conservation planning, but frequently, conservation decisions are made without surveys, due to lack of time, funds, or appropriate methodology. A method. target taxon analysis, is therefore proposed for streamlining regional biotic inventories. while simultaneously increasing their taxonomic coverage and spatial resolution. In this method, regional inventories focus on a number of narrowly defined target taxa, chosen to represent collectively an array of higher taxa. Such target taxa should be information rich: in other words, the pattern of species distributions in these taxa should correlate either with patterns of environmental heterogeneity or with distributional patterns of species in unrelated taxonomic groups. It is suggested that clades that experienced an evolutionary radiation within the region are likely to be information rich for conservation planning at or within this regional scale. Such clades will be identifiable as low-ranking, species-rich taxa with high endemism. The information richness of these potential target clades can then be evaluated by direct gradient methods of analysis that relate community compositional change to environmental factors, or by correlating distributional patterns of species among separate target clades. To assess this approach to biological inventory, a species-rich genus and subgenus of endemic butterflies from the island of Madagascar were chosen as target taxa and were evaluated for information richness in comparison to the entire butterfly fauna of Madagascar. Using canonical correspondence analysis and other analytical techniques, the subgenus of Malagasy Henotesia species (Satyrinae) proved to be as good or better than the entire butterfly fauna at delineating a variety of environmental gradients at both local and landscape scales. The endemic genus Strabena (Satyrinae) was only able to delineate such patterns under a restricted set of conditions. However, this genus, while species rich in Madagascar, was not exceptionally diverse nor were its species members abundant within the study area. It is concluded that target taxon analysis is a potentially useful tool for providing high-quality data while expanding coverage of taxonomic diversity for conservation planning.