=Paper=
{{Paper
|id=Vol-1152/paper6
|storemode=property
|title=A New Method to Evaluate Habitat Status Based On the Use of Data On Oribatid Mites (Acari: Oribatida)
|pdfUrl=https://ceur-ws.org/Vol-1152/paper6.pdf
|volume=Vol-1152
|dblpUrl=https://dblp.org/rec/conf/haicta/GergocsH11
}}
==A New Method to Evaluate Habitat Status Based On the Use of Data On Oribatid Mites (Acari: Oribatida)==
https://ceur-ws.org/Vol-1152/paper6.pdf
A New Method to Evaluate Habitat Status Based on
the Use of Data on Oribatid Mites (Acari: Oribatida)
Gergócs, V.1 and Hufnagel, L.2
1
Department of Mathematics and Informatics, Corvinus University of Budapest, Faculty of
Horticulture
H-1118 Budapest, Villányi út 29-43., Hungary
(phone: +36-1-482-6261; fax: +36-1-466-9273)
2
“Adaptation to Climate Change” Research Group, Hungarian Academy of Sciences, Office
for Subsidised Research Units
H-1118 Budapest, Villányi út 29-43., Hungary
(phone: +36-1-482-6261; fax: +36-1-466-9273)
Abstract. Several indicators have been available for evaluating the
coenological-ecological status of habitats. Of these, functions that are simple
to use in a standardized way are of great importance in environmental biology.
One such method involves listing the genera occurring in a given habitat with
large abundance and species richness. In our study, the indication power of
genus-level lists of Oribatid mites and the underlying effects behind the
generation of similarity patterns were analysed using data on Oribatid mites
collected by ourselves and derived from the literature. Our objective was to
develop a method by which the distance between two Oribatid mite genus lists
originating from any sources is evaluated for correspondence to spatial scales.
Keywords: Oribatid mites, genus list, family list, distance function,
indication, pattern generation
Introduction
There have been only a few efficient tools to express objectively and numerically
the current state and naturalness of a given habitat. This poses a huge problem in
conservation practice since this information is essential for decision-makers to judge
properly to what extent a habitat is disturbed and if it needs protection. To overcome
this problem, suitable indicator groups of organisms and methods should be
established.
The main goal of this study is to set up a comparison scale based on genus-level
presence-absence data of Oribatid mite communities (Acari: Oribatida) from habitats
examined at different spatial and temporal scales. The secondary goal – and this
time the precondition as well - is to get a reliable picture on the indication power of
the distances to be used.
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63
The use of Oribatid mites as indicators for describing the status of their habitat is
justified by the special characteristics of the group. Oribatid mites can be found in
almost all kinds of habitats: on land and in water; first of all in the organic horizons
of soils. However, they have penetrated into different other microhabitats as well
(e.g., lichens, bryophytes, bark etc.), which is mainly due to the great variability of
their food sources (e.g. organic debris, fungi, other mites, etc.). In addition to the
diversity of habitats, their high adaptation ability is also shown by their enormous
abundance and species richness. The above features may be utilized by using
coenological methods (Lebrun & van Straalen, 1995; Behan-Pelletier, 1999; Gulvik,
2007; Gergócs & Hufnagel, 2009).
The choice of the genus level is reasonable ecologically. Caruso & Migliorini
(2006) have shown that there were no significant changes in the data on the
anthropogenic disturbance on Oribatid mites when switching from species level to
genus level. Our study has a similar objective: we would like to see how potential
habitat changes are indicated based on genus level lists. Podani (1989) had a similar
observation in case of plants, according to which switching to genus level did not
cause significant change when comparing the examined habitats. Osler & Beattie
(1999) carried out a meta-analysis, which confirmed our assumption that taxonomic
levels above the species are suitable for comparing habitats. This research showed
that habitats can be distinguished on family level as well in case of Oribatid mites,
therefore our study also covers family level besides the genus level. There were also
some other arguments in favour of this decision, namely that the number of
databases used could be considerably extended in this way. In addition, taxonomical
processing could become faster and more reliable in our field studies as well. Genus-
level identification of Oribatid mites is solved on the basis of the work by Balogh &
Balogh (1992) on a global scale, too. However, species-level identification is only
possible for some zoogeographical regions and only some taxa on a global scale
since the related literature is not synthesized yet properly (e.g. Balogh & Mahunka,
1983; Olsanowski, 1996).
By setting up the spatial and temporal scales, we expected that comparisons
order of Oribatid mites’ habitats based on the genus and family lists corresponds to
actual spatial and temporal scales, i.e. the farther and qualitatively the more different
habitats our lists originate from, the greater difference is found among similarities
inside the given categories. However, if data originate from the same site, the
difference among the examined samples should be greater in case of the lists which
are more distant in time from each other.
The main goals of the present study are the following.
1. Developing a spatial and temporal scale reference based on the genus- and
family-level taxon lists with the help of similarity functions.
2. Examining the degree of distances in the similarity order used for
indication.
3. Utilizing the distances for comparing the habitats being under the effects of
human perturbations with natural habitats measuring the extent of disturbance.
64
Materials and Methods
Categories and sources of the genus lists. In order to be able to determine to
which spatial and temporal distance the Oribatid mite genus lists of two
samples/sites correspond, different categories had to be defined (Gergócs et al.
2010). These categories were set up considering the combination of the given spatial
and temporal scales the examined pair of genus lists originates from. These
combinations were as follows.
First of all, some categories originate from the same zoogeographic kingdom, the
same topographic position (i.e., country) and the same type of habitat, so we will not
sign them in the codes of these categories. The first category originates from a
homogenized, parallel sample collection (HPS- from own research). Also our own
study from Hungary made it possible to set up categories on pattern levels meaning
a distance of 2, 12, 24 and 52 weeks, in which substrate-microhabitat (S), habitat
(H), topographicum and zoogeographic kingdom were the same (Sa). Regarding the
time (Ti), we differentiated these categories: SaS/Ti-2, SaS/Ti-12, SaS/Ti-24 and
SaS/Ti-52.
Samples were collected from three different places in Hungary to compare
several habitat types and substrate types: 1) bank of the Danube: a floodplain forest,
a meadow and a Black Locust (Robinia pseudo-acacia) plantation. 2) Velence
mountains: a dry oak forest, a mossy thermophilous Quercus pubescens wood, a
European hornbeam forest. 3) rség district: a spruce plantation, a hornbeam-beech
forest, a meadow.
Two categories were made from the data described above: SaS/Hu/close and
SaS/Hu/far. These codes mean that the same type of substrates in the same type of
sites were compared with the same type of substrates that belonged to a closer or a
farther (being several kilometres far from the other site) site of the same type.
Data of the last category of the same substrates (SaS-trop) were collected from
the tropics, by Janos Balogh. Data of Oribatid mite genus lists are from a moss
forest in Costa Rica, a rain forest and a paramo in Brazil, and a rain forest in Papua
New Guinea.
The next change in scale is the difference in substrate: DS. Two groups of these
genus lists originate from our own database from the temperate zone (DS-temp) and
from the above mentioned manuscripts by Balogh (DS-trop). The other two
categories were made from the database of other sites in Hungary differentiating the
distance in same types of sites: DS/Hu/close and DS/Hu/far.
Genus lists belonging to the same types of tropical and temperate habitats (SaH-
trop, SaH-temp) were obtained from the manuscripts by János Balogh (Australia, Sri
Lanka, Papua New Guinea, Costa Rica and Ecuador), the study by Migliorini et al.
(2005) and the studies by Hammer (1958, 1961, 1962, 1966).
Sources of the categories of different habitats (DH-trop, DH-temp) are: studies
by Noti et al. (1996), Migliorini et al. (2002), Osler & Murphy (2005), Skubala &
Gulvik (2005), Arroyo & Iturrondobeitia (2006), Osler et al. (2006), manuscripts by
János Balogh, published series by János Balogh (Balogh et al., 2008) and studies by
Hammer (1958, 1961, 1962, 1966). A series belonging here originates from samples
collected by Levente Hufnagel in Australia (2006, Australia: QLD, Cairns).
65
At this level, the genus lists from Hungarian habitats were examined as well.
Accordingly, the following categories were made: SaH/Hu/far, DH/Hu/close,
DH/Hu/far. SaH/Hu/close could not be created because of missing data.
In case of genus lists originating from different topographic positions (practically
countries, DT), we considered the point if they originated from the same
(SaK/DH/SaH) or different sites (SaK/DT/DH) and if the two topographic positions
were in the same or different zoogeographic kingdoms (DK/DT/SaH, DK/DT/DH).
These series came from studies by János Balogh and Marie Hammer.
In the last category, the complete genus lists of the six zoogeographic kingdoms
were compared according to the work by Balogh & Balogh (1992) (DK).
Applying the reference list. Adaptability of our results will be demonstrated by
showing some examples from other papers. In order to get genus lists from species
lists we used five publications comparing natural mite assemblages with Oribatid
mite communities destroyed by human disturbance. Hülsman & Wolters (1998)
evaluated the effects of three tillage practices on soil mites in a replicated field
experiment. Zaitsev & van Straalen (2001) made a study of Oribatid mite
communities and their responses to metal contamination. Andrés and Mateos (2006)
used soil mesofaunal bioindicators to evaluate four post-mining restoration
treatments. Surveying the efficiency of treatments was carried out after 12 years by
examination the soil mesofaunal responses. Berch et al. (2007) studied the responses
of Oribatid mite species to site preparation treatments in high-elevation cutblocks.
Déchene & Buddle (2009) tested how different experimental harvesting regimes
affect the diversity, abundance and composition of Oribatida in a forest in Canada.
Descriptions of the compared sites can be seen in Table 2.
The similarity values of the genus list pairs created from the above papers were
obtained with the Ochiai function. On each occasion the genus list of control sites
was compared with genus lists of treated sites. Finally, the distance data calculated
in this way were confronted with the values of reference list checking which
category suits the distance between control and treated sites. Sites in the studies
mentioned above always originated from the same type of substrates and
topographic positions (countries).
Data processing. From the databases we did not consider all possible list
combinations which fit the category, only the ones having at least nine genera. After
our complete genus list database was set up, the number of genera of the two lists
and the numbers of the common genera were determined considering the genus list
pairs in each category. As we had only presence-absence data and the value “d” of
the contingency table was not considered in the case of the genus list pairs, the
Ochiai and Jaccard functions were used as similarity functions (Podani, 1997). The
similarities in each category were calculated from the means of the values of the
similarity functions for the genus list pairs.
As our data were not always independent within a category, it was determined
with a complex method to what extent the means of the categories differ from each
other.
As there were few data in categories from Hungary we concentrated them with
other adequate categories e.g. DS/Hu/close and DS/Hu/far with DS-temp. Since
there was no SaD-temp category we made one from the categories SaS/Hu/close and
66
SaS/Hu/far for this examination. So, the original 24 categories reduced to only 18
categories. We had several distance function values within each category as we had
85 genus list pairs within one category on average. From among the distance
function values of each category, fifteen values were chosen randomly with the help
of a random number generator in the Excel software. It was carried out ten times in
case of each category. In this way we got 10 series containing 15 values for each
category. Series of the data table containing 10×15 values in case of each category
were now independent and since normal distribution could not be observed within
each category, the data were analysed with the Kruskal-Wallis statistical test using
PAST software (Hammer et al., 2001). Each of the 18 series was analysed with the
Mann-Whitney post hoc test as well, so we got ten tables containing 18×18 post hoc
test results. One table (Table 1) was made out of these ten tables, which shows that
how many times there are significant differences among the ten results at the 5%
significance level. Based on this we were able to decide which categories differ from
each other significantly.
The above analyses were carried out at the family level as well.
Results
Order of genus and family list categories. Figure 1 displays intervals with
standard error around the Ochiai distance means in case of each category.
The category of homogenized parallel sampling (HPS) shows the outstanding
largest similarity between the samples. This was followed by the samples originating
from same (SaS) and different (DS) substrates. These two types of categories do not
differ from each other because their orders are mixed. Similarities of genus lists
originating from different times are the next: first the two-week-difference, then the
12-, 24- and finally the 52-week-difference. There is greater difference between
genus compositions of Hungarian samples originating from the same type of habitat
being in large geographical distance but originating from the same or different
substrates (.../far) than in case of samples from closer habitats (.../close). Categories
of tropical samples on substrate level are further back than the categories from the
temperate zone comparing among each other with less geographical distances. The
similarities of the “.../far” samples originating from different substrates are low so
these categories have fallen amongst the categories differing in site level.
An opposing phenomenon can be seen on habitat level (SaH and DH). Habitats
from the tropics are more similar to each other than the habitats from the temperate
zone. The geographical distance has reduced the similarity between communities
also on habitat level since the further habitats are more different from each other
than the closer habitats. Same and different types of habitats have not been sharply
separated from each other. Oribatid genus lists from different types of habitats in the
same zone tend to be less similar to each other than the genus lists from the same
habitat types.
67
Fig. 1. The order of categories at the genus level. Similarity increases from right to left. For
codes, see text.
In the last “block” showing the lowest similarities are categories measuring the
difference of genus lists on habitat level between topographic positions (DT). The
same type of habitat (DT/SaH) shows greater similarity than the different habitat
(DT/DH) irrespectively of that the positions are either in the same zoogeographical
kingdom or in different ones (SaK-DK). The category DK is among these
categories. We got nearly the same results using both similarity functions (Ochiai
and Jaccard), only the order of three pairs of categories has been inverted by the
Jaccard function, but this condition does not cause inconsistency with statements
mentioned above.
The results on family level largely correspond with the results on genus level.
There is one notable difference from the results on genus level. The category DK has
fallen amongst the categories of substrate level.
Significance of distances between genus and family list categories. The category
of homogenized parallel sampling (HPS) is isolated from all the other categories
(Table 1). The ten randomly chosen data often show different results for the
separation of categories. The categories of same substrates (SaS) stand close to each
other and to the categories of different substrates (DS). The uncertainties begin with
the separation of categories of SaH and DH from the category groups of SaS, DH
and DT. The categories of different topographic positions (DT) go together very
much but they are uniformly separated from the categories of same and different
substrates (SaS and DS). The categories of the same and different habitats (SaH and
DH) fluctuate between the two large blocks, i.e. they vary if they are close either to
the block of DT categories or to the block of SaS/DS. Distinction of tropical and
temperate zones on the given habitat level is of importance only by different habitats
68
(DH), e.g. DH-temp and DH-trop often separate from each other significantly. We
got the same results on family level as on genus level like in the case of orders. The
DK category is significantly different from the block of different topograchica (DT).
Table 1 The significance of differences between the 18 genus list categories according to
Mann-Whitney tests. The numbers in the table mean that how many times there are significant
differences between the categories among ten results at p=0.05.
DK/DT/SaH
SaK/DT/DH
DK/DT/DH
AB/ET/AS
SaS/Ti-12
SaS/Ti-24
SaS/Ti-52
SaH-temp
SaS-temp
DH-temp
SaH-trop
DS-temp
SaS/Ti-2
SaS-trop
DH-trop
DS-trop
18
DK
categories
HPS 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
SaS/Ti-2 0 2 4 5 8 7 9 10 10 10 10 10 10 10 10 10
SaS/Ti-12 3 3 3 4 5 7 10 10 10 10 10 10 10 10 10
SaS/Ti-24 0 1 0 1 3 9 10 10 10 10 10 10 10 10
SaS/Ti-52 0 1 0 4 6 7 6 10 10 10 10 10 10
DS-temp 0 0 0 1 6 8 10 10 10 10 10 10
SaS-trop 0 1 3 5 5 10 10 10 10 10 10
SaS-temp 0 3 6 7 10 10 10 10 10 10
DS-trop 1 3 4 9 10 10 10 10 10
SaH-trop 0 2 7 6 10 9 9 10
DH-trop 1 5 4 10 10 9 10
SaH-temp 5 4 9 9 8 10
DH-temp 0 0 0 0 3
SaK/DT/SaH 0 0 1 7
DK 1 0 3
DK/DT/SaH 0 4
SaK/DT/DH 1
Applying the created reference list. Dissimilarities for the genus lists of the five
publications are shown in Table 2. It can be seen that how much change was caused
by several human perturbations in the composition of Oribatid community in a given
habitat. Several human interventions examined by Hülsman and Wolters (1998),
Déchêne and Buddle (2009) and Zaitsev and van Straalen (2001) did not cause much
change in the composition of Oribatid communities since the differences between
the analyzed habitats are as much as the differences between the communities from
the same habitat and the same substrate (SaS).
However, Déchêne & Buddle (2009) found that clear cut and burning after
harvest have large effects on Oribatid mite communities. In this case the distance
between the communities are up to the distance either in case of between same
habitats or in case of between different types of substrates. We could find similar
results by the perturbations in (Berch et al. 2007) namely not only burning but
mounding and scalping the soil have important effects on Oribatid communities. The
greatest differences could be measured in case of succession in a post-mining
restoration some years after the beginning in the study of Andres and Mateos (2006).
In this study, samples originating from adjacent natural habitats differ as much from
the Oribatid genus lists of post-mining habitats as the habitats from the same or
different topographica differ from each other.
69
Table 2. Ochiai dissimilarities between the genus lists from the five publications. Table
shows the attributes of the compared sites and the categories having the same values of
distances.
Ochiai Categories
Sources Control sites Treated sites function correspond to
values values
conventional tillage with a
no soil cultivation 0,84
Hülsman mouldboard plough
&
no soil cultivation soil cultivation with a chisel plough 0,84 HPS
Wolters
(1998) minimum tillage with a springtine
no soil cultivation 0,84
cultivator
at a distance of 10 km at a distance of 3 km from the
0,83
Zaitsev from the smelter* smelter *
& van at a distance of 10 km at a distance of 2 km from the
0,73 HPS
Straalen from the smelter* smelter *
(2001) at a distance of 10 km
close to the smelter* 0,78
from the smelter*
SaK/DT/SaH,
neighbouring
soil spreading 0,44 DH-temp, DS,
unexploited forest area
SaH/Hu/far
Andrés neighbouring soil-spreading+ grass and herb
& 0,54
unexploited forest area sowing
Mateos DH-trop,
neighbouring
(2006) soil spreading + tree planting 0,56 SaH-temp,
unexploited forest area
DH/Hu/close
neighbouring
soil spreading+ sowing+ plantation 0,52
unexploited forest area
untreated forest floor burned 0,63
Berch et untreated mineral soil burned 0,55 SaS, DS, SaH-
al.
untreated mineral soil mounded 0,61 trop
(2007)
untreated mineral soil scalped 0,55
control site (leaf litter) one-third partial cut 0,88
control site (leaf litter) two-third partial cut 0,89
Déchêne control site (leaf litter) clear cut 0,87 SaS ; HPS
& control site (leaf litter) controlled burn-after-harvest 0,84
Buddle control site (soil) one-third partial cut 0,69
(2009) control site (soil) two-third partial cut 0,71
control site (soil) clear cut 0,61 SaS, DS
control site (soil) controlled burn-after-harvest 0,53 SaS, DS
Discussion
Order of the genus list categories. Prominent similarity of the homogenized
samples is not extraordinary, but it is important that they do not show the maximum
(i.e., 1). Consequently if there is no difference between two samples there can be
some deviation caused by sampling or by accidence. It can be assessed that two
70
samples are different only when the distance between them is less than the value of
the HPS category.
It can be clearly seen that genus lists differed only in substrates are more similar
to each other than comparing whole sites or sites differing in topographica with each
other. We would have expected that the distance between different substrates (DS)
would be larger than between same substrates (SaS). This result was shown at the
order of the categories but it was not significant. Karasawa and Hijii (2004) showed
that the species composition of Oribatid communities in mangrove forests is more
likely to be affected by factors responsible for microhabitat diversity than by
geographical distance between the examined islands. It means that the same type of
microhabitats on two distant sites may be more similar to each other than two
different substrates on the same site.
We did not see large changes in Oribatid mite community composition living in
a given substrate type after a year. The result met our expectation but the separations
of the categories were not significant. In our study, we could examine a period of not
more than a year difference between genus lists but when an Oribatid mite
community was monitored in a beech forest for 6 years by Irmler (2006), he found
more than 75% similarity in the communities of various years.
In the tropics, substrates are more different from each other than in the temperate
zone. At the level of habitats tropical sites are more similar to each other than the
sites in the temperate zone. According to these two results in the temperate zone the
Oribatid genus lists of microhabitats are more homogeneous than in the tropics, as in
the tropics the genus lists of habitat types are more uniform. But since the
differences are not significant between the results of tropical and temperate zone,
this can signify only a trend.
Categories of different topographica (DT) were unambiguously separated from
the categories of substrate level. Data of substrate and habitat levels from different
sites in Hungary showed that geographical distances can cause large differences
between Oribatid genus compositions. Zaitsev and Wolters (2006) studied the
impact of climate across Europe on the structure and diversity of Oribatid
communities. They found that at the chosen scale climate had moderate impact on
abundance and biomass of Oribatida communities showing that some other
important factor(s) cause(s) larger difference between species and as we could see
between genus compositions in larger level of geographical distance.
On genus level, distance between zoogeographic kingdoms means the same
difference as it is between genus lists originating from different countries
(topographica). It is remarkable that zoogeographic kingdoms were mainly
differentiated based on vertebrate groups and if an invertebrate group, in this case
Oribatid mites are regarded, difference between zoogeographic kingdoms on
vertebrate level can cover smaller topographica and not continents in case of
Oribatid mites. On family level, distance between zoogeographic kingdoms means
the same difference as between family lists originating from the same or different
types of substrate. In case of Oribatid mites, zoogeographic kingdom is not a
reasonable unit of differentiation on family level, while it is one of the units of
differentiation in case of vertebrates, which is most likely due to the fact that
71
separation of Oribatid mite families historically preceded the separation of
continents.
Analyzing different topographica we found that the Oribatid communities living
in the same type of habitats resembled each other much more than in case of
communities living in different habitats when genus lists were compared between
different zoogeographical kingdoms. This confirms our previous assumption that the
type of a habitat may play a greater role in pattern formation than the
zoogeographical kingdoms. Balogh et al. (2008) obtained the same results.
The order of assay on family level often differs from the order on genus level,
but these differences are mostly by categories where distances are not significant so
the differences are not considerable. Similarities between results of family and genus
level point out that it is possible to use the family level instead of the genus level
from a taxonomical point of view in the comparisons suggested by us. This
corresponds to the results of the meta-analysis by Osler and Beattie (1999) – similar
to ours – in which it was found after the analysis of 25 studies that habitats were
mainly chosen on family level and they suggested that the family level could be
enough to quickly estimate the diversity of an area.
We could observe that certain human disturbances would not cause big changes
in Oribatid communities as if we repeated sampling from the same substrate.
Increasing perturbation by tillage practices (Hülsman and Wolters, 1998), effects of
metal contamination in different distances from a smelter on Oribatid communities
(Zaitsev and van Straalen, 2001), and at last the effects of experimental harvesting
regimes after eight years on Oribatid mites in a mixed boreal forest (Déchêne and
Buddle, 2009) are cases which point to the above mentioned small changes. By the
other cases we could be allowed to examine larger distances caused by perturbation:
burning after clear cut harvest in a temperate deciduous forest (Déchêne and Buddle,
2009), burned, mounded and scalped forest floors (Berch et al. 2007) and an
inchoative stage succession of post-mining restoration (Andrés and Mateos, 2006).
By means of the standardized reference list shown in this study we can count
how much spatial distance is equivalent to the similarity of genus or family lists of
Oribatid communities originating from two unknown samples. Our results show that
measures of human disturbance can be correlated with spatial differences by means
of our reference list, namely expressing the effect of perturbation on the composition
of Oribatid communities.
Examining Oribatid mites is important because of their special properties, but
their usefulness in describing the stage of habitats has not yet been exploited. Data
about them are being assembled but often in an uncoordinated way and they cannot
be compared properly. Using Oribatid mites suitably requires effective and
systematic data recording which is standardized and coordinated. Integrated
processing and interpretation of huge databases should be performed in a way the
present study demonstrated.
Acknowledgements. The authors gratefully acknowledge the contribution of Prof.
János Podani for his kind methodological help, Dr. Péter Balogh for his valuable
professional help with the research of Oribatid mites and for making the manuscripts
from the Balogh-legacy available. We thank the “Adaptation to Climate Change”
72
Research Group of the Hungarian Academy of Sciences and particularly the late
Zsolt Harnos, who ensured the professional prerequisites of the research. Our
research was supported by Ányos Jedlik Project No. NKFP-0079/2005 (National
Office for Research and Technology), the Research Assistant Fellowship Support
(Corvinus University of Budapest) and the “Bolyai János” Research Fellowship
(Hungarian Academy of Sciences, Council of Doctors). This work was supported by
the research project of the National Development Agency TÁMOP 4.2.1.B-
09/1/KMR-2010-0005.
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