Biodiversity and Conservation 12: 851882, 2003. 2003 Kluwer Academic Publishers. Printed in the Netherlands.

Assessing tillage disturbance on assemblages ofground beetles (Coleoptera: Carabidae) by using arange of ecological indices

1 2, 3*SVENJA BELAOUSSOFF , PETER G. KEVAN , STEPHEN MURPHY and4CLARENCE SWANTON

1School of Rural Extension Studies, University of Guelph, Guelph, Ontario, Canada N1G 2W1;2Department of Environmental Biology, University of Guelph, Guelph, Ontario, Canada N1G 2W1;3Department of Environmental and Resource Studies, University of Waterloo, Waterloo, Ontario,

4Canada N2L 3G1; Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada*N1G 2W1; Author for correspondence (e-mail: pkevan@uoguelph.ca; fax: 11-519-837-0442)

Received 14 June 2001; accepted in revised form 29 April 2002

Key words: Carabid beetles, Disturbance, Diversity and abundance distributions, Diversity indices,Tillage

Abstract. Many ecological studies have used diversity indices to assess the impact of environmentaldisturbance. In particular, ground beetles have been advocated as a good group for assessing disturbance.Most studies on various organisms have used only one or two indices. For our study of the impact oftillage disturbance on carabid beetles in farm fields in southern Ontario, Canada, we used seven differentdiversity indices (richness, ShannonWiener, BergerParker, Q-statistic, Margalef, a and evenness).Few studies have used deviation from diversity abundance models as a measure of disturbance; however,we use three that are applicable to our data (geometric, log-normal and log-series). The indices andmodels were used to test the null hypothesis that there is no change in diversity with increasing tillagedisturbance, and that there is no difference in diversity with different crops or years. We were not able toreject the null hypothesis that there is any diversity difference among farms. We also found that there wasno single diversity index or model that was better than any other at detecting disturbance. These resultsare supplemented by a meta-analysis of 45 published data sets for the same taxon but in different habitats.The meta-analysis supports the conclusions from our field research that diversity indices and models arenot useful for detecting the possible effect of disturbance on assemblages of carabid beetles.

Introduction

Ecologists often use changes in species diversity to determine the effects ofdisturbance because it is an important component of any ecosystem (May 1975;Hutchinson 1978; Magurran 1988). To determine the effect of disturbance onspecies diversity it is necessary to use appropriate species assemblages as indicatorsof disturbance. Species assemblages above the single species level are communities,functional groups, guilds and taxocenes (Belaoussoff 2000). In this study we usedfunctional groups. Functional group can be defined as a group of not necessarilyrelated species exploiting a common resource base in a similar fashion. Within afunctional group there is greater similarity in ecological resource requirements thanwithin a guild, thereby implying that there is a greater degree of interspecific

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competition (Arthur 1984; Colwell and Winkler 1984). Some examples of func-tional groups include pollinating bees (Kevan et al. 1997), livestock gut fauna (Izsakand Hunter 1992), and herbivorous Macrolepidoptera (Laroca et al. 1989; Hill et al.1995).

We chose to use functional group because there is an overlap in resourcerequirements between species in a functional group. Disturbances would affectthose species evenly by disrupting resources that they all use. Species withincommunities and guilds often depend on different resources. Hence, a givendisturbance would affect some of those resources but not all, and therefore not allspecies would be equally affected. Because the species assemblages which make upcommunities and guilds are not equally affected by disturbances, they would not beas sensitive to disturbance as functional groups would be.

There are two different approaches to assess the effect of disturbance on specieswithin functional groups: differences in diversity indices, and deviation fromdiversity and abundance models. Of the two, diversity indices are more frequentlyused by ecologists. Diversity indices are generally simple to calculate (with someexceptions, e.g., the Q-statistic; also see Magurran 1988), especially in comparisonto the mathematics and statistics behind diversity and abundance models. Althoughthere are many different diversity indices, they generally fall into one of twocategories: species richness indices, and indices based on the proportional abun-dances of species (Magurran 1988). Richness indices are a measure of the numberof species, or individuals, in a defined sampling unit (e.g., number of species,density, abundance). Indices which measure the proportional abundance of speciescombine species richness and evenness (the component of species diversity thatmeasures the relative abundance of species) into a single figure (Magurran 1988)(e.g., ShannonWiener, BergerParker, Q-statistic, Margalef, a and evennessitself). These indices tend to be biased either towards species richness (e.g., a,Q-statistic) or dominance (e.g., BergerParker) (Magurran 1988).

Diversity indices can be useful because they provide rapid, and easily calculated,ecological measures. Also, because many researchers use indices, it is possible tomake some comparisons between similar studies which use the same indices.Nevertheless, there are three main shortcomings of these indices. First, they can beused only for comparisons between sites, or on sites over time, or both [for e.g.,studies on the effect of mining waste on fish (Cornell et al. 1976), landfillreclamation on invertebrate populations (Judd and Mason 1995) and altitude onspider populations (Otto and Svennson 1982)]. Second, diversity indices are onlystatistical artifacts and do not have any intrinsic biological meaning (Southwood1978; Magurran 1988). Third, the use of different indices with the same data canresult in different conclusions (Belaoussoff 2000).

One way of overcoming weaknesses in diversity indices is to have a diversitymeasure which is stand-alone and does not need inter-site or inter-time com-parisons. With such a stand-alone measure, a theoretical standard against which tomake comparisons is required. To avoid the shortcoming of being a statisticalartifice, the standard should be based on community structure, as well as ecologicaland evolutionary theory. It is possible to compare the structure of a functional group

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against the standard and to assess whether or not the functional group is disrupted bya disturbance. Theoretically, if the structure of the functional group fits the standard,then it is not disturbed. If it deviates from the standard, then the group is affected bythe disturbance. Diversity and abundance models, and distributions of size withinfunctional groups, provide a good starting point to find the standards needed forstand-alone measures of diversity, because they are based in ecological andevolutionary theory.

Although diversity and abundance models have been present in the ecologicalliterature for over 80 years (e.g., Raukiaer 1918), diversity indices are morefrequently used by ecologists to examine the effect of disturbance on speciesdiversity (Southwood 1978; Magurran 1988). Relationships describing the diversityand relative abundance of species in a community were originally suggested byRaukiaer (1918), and then again by Motomura (1932). Although different diversityand abundance models have been developed (e.g., Mandelbrots (1977) ZipfMandelbrot model; Dewdneys (1997) logistic J and Hubbles (personal communi-cation) zero-sum multinomial), four principal distributions of diversity and abun-dance (MacArthurs broken stick model, geometric series, log series and lognormal) remain central to discussions about diversity and abundance models(Magurran 1988). Because those four distributions are commonly considered, theyare the ones used in this study. Furthermore, some of the alternate models are notbased on ecological principles [e.g., the ZipfMandelbrot model is based oninformation theory (Magurran 1988)], or are empirical fits of data which cannot beseparated from log normal distributions [e.g., the logistic J model (Dewdney1997)].

The broken stick model reflects equally divided niche space (MacArthur 1957).Its biological meaning is not obvious (Hutchinson 1978; Magurran 1988), althoughempirically it is most likely to occur in groups of somewhat mobile animals living ina homogeneous environment. Unlike the other models, this model reflects anequitable state (Magurran 1988) and there is no competition between members ofthe functional group. The value of the broken stick model has been challenged(Wilson 1993), and there are few examples of where it occurs [e.g., passerine birds(MacArthur 1960) and minnows (King 1964)].

Both the geometric and log series distributions are believed to result when nichepre-emption occurs [i.e., a few species pre-empt, or occupy, most of the niche(Hutchinson 1978)]. In these distributions few species dominate, and those fewspecies occupy a large portion of the available niche space. Field data have shownthat geometric distributions are found in species-poor (often harsh) environmentswhich are in the very early stages of succession (Whittaker 1965, 1972, 1977) orunder disturbed conditions (e.g., Kempton and Taylor 1974). As successionproceeds, or as conditions improve, species abundance distributions become logseries (Magurran 1988).

The log normal relationship describing the diversity and relative abundance ofspecies in a community is one of the oldest proposed (Raukiaer 1918; Motomura1932). In communities described by this distribution there are few common species,more less common species, and few rare species. It was Preston (1948) who first

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tested the idea that species diversity and abundance can be described by lognormally distributed frequencies. Since then the model has been shown to fit manybiological communities [e.g., diatoms (Patrick 1968); soil arthropods (Hairston andByers 1954); Macrolepidoptera (Kempton and Taylor 1974; Laroca and Mielke1975; Laroca et al. 1989; Hill et al. 1995), birds and mammals (Preston 1962); bees(Mackay and Knerer 1979; Tepedino and Stanton 1981; de Bortoli and Laroca 1990;Barbola I. de and Laroca 1993; Stubblefield et al. 1993; Kevan et al. 1997) andSalmonella strains in livestock (Izsak and Hunter 1992)].

The log normal model of diversity and abundance is more than a statisticalproperty of sampling (cf. May 1975). Sugihara (1980) suggested that the niche spaceof a taxonomically related group is sequentially split by the constituent species, andthe fraction of the niche space apportioned to a species is proportional to itsabundance. The log normal model, as described by Sugihara, is also known asSugiharas sequential breakage model. Until Sugihara (1980) broached its bio-logical rationale, the log normal model was considered to be a statistical conse-quence of the Central Limit Theorem (MacArthur 1960; May 1975; Tokeshi 1993).The model represents an organizational pattern for a functional group, and is basedon the following considerations (Sugihara 1980):1. the existence of an hierarchical niche structure in the functional group;2. the underlying structure of niches is reflected in the pattern of abundance;3. it is applicable to functional groups.

A hierarchical niche structure implies that each species making up a functionalgroup is different from the others and none is redundant. Tilman and Downingsstudy (1994; see above) supports that idea and links diversity to ecosystem stability.

Because the log normal model of diversity and abundance has biologicalmeaning, it can be used to address two ecological issues: (1) how to quantitativelydefine a functional group, and (2) the need for an indicator of disturbance which isfree from temporal, or spatial, comparisons of species. Generally, researchersclassify species assemblages as functional groups, guilds or taxocenes because thespecies within the assemblage are closely related, occur together in a particularhabitat or seem to have similar ecological niches. Competition occurs betweenconstituent species of functional groups, thus it is possible to assess them quantita-tively. It is useful to employ quantitative methods to describe a functional group inorder to give the definition greater scientific validity than if it is merely based on

subjectively grouping species together (Jaksic 1981). By using a fit to the log normalmodel under undisturbed conditions it may be possible to assess quantitatively if afunctional group is present. If this way of describing a functional group is not usedwith caution, it is possible to place more than one functional group together and stillobtain a log normal distribution of diversity and abundance. Even so, this way ofdescribing a functional group is an initial, and much needed, quantitative approachto determining if a collection of species form a functional group. Researchers usinglog normality to define functional groups have to be conservative, and cognisant ofthe ecological backdrop in which log normality is used.

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Preston (1980) proposed that the degree of deviation of diversity and abundancefrom log normality can be used as an index of the degree of disturbance. The ideathat deviation from log normality might reflect the effect of disturbance has beentested with diverse assemblages of marine organisms (Gray and Mirza 1979; Gray1979, 1983). Those studies supported the hypothesis that there would be deviationfrom log normality, even though the organisms that were sampled did not belong toa single functional group. Thousands of individuals were collected, and it is likelythat several functional groups were each well represented and were all similarlyaffected by the pollution examined by Gray (1983) and Gray and Mirza (1979). Todate, studies which have associated departure from log normal distributions ofdiversity and abundance with disturbance have been published on pollinating bees(Kevan et al. 1997), Lepidoptera (Laroca et al. 1989; Butler 1992; Butler et al. 1995;Hill et al. 1995), gut bacteria in livestock (Izsak and Hunter 1992), and soil

arthropods (Hagvar 1994). Using deviation from species diversity and abundancerelationships to determine whether or not a biological community is disturbed iscontroversial (Lambshead et al. 1983; Shaw et al. 1983; Nelson 1987). In general,the major critics of the method (e.g., Nelson 1987) have lumped unrelated, and/oronly partial, functional groups together, thereby dismissing the approach withoutunderstanding why it did not work.

Functional groups exposed to unexpected disturbance events are expected todeviate from log normality. Such deviation merely raises a red flag, which indicatesthat the functional group in question may have been disrupted (Kevan et al. 1997;Belaoussoff and Kevan 1998). According to some researchers, log normality is acharacteristic of communities in equilibrium (Stenseth 1979; Ugland and Gray1982). However, a study on birds shows that undisturbed forest habitats are notnecessarily at equilibrium (Gee and Giller 1986). Thus, situations exist in whichfunctional groups in undisturbed habitats might deviate from expected log normali-ty. Deviation of functional groups away from theoretical standards which areprovided by diversity and abundance is a relatively new approach to studying theeffects of disturbance. As well, deviation of size within functional groups is a novelapproach. More work is needed to sort out the complexities of functional groups anddeviation from log normality as an indicator of disturbance (Hill and Hamer 1998).

Our study examines methods to quantitatively define functional groups withrespect to Carabidae (i.e., carabid or ground beetles) on farms representing agradient of tillage disturbance. Carabidae need to be examined as a potential groupfor assessing disturbance because they are considered to be sensitive indicators of

environmental change (e.g., Dritschillo and Wanner 1980; Sekulic et al. 1987; Eyreand Ruston 1989), they are abundant within a wide range of ecosystems (see Thiele1977), they are relatively easy to catch in large numbers (Thiele 1977), thetaxonomy of many species is well known (Lindroth 19611969) and expertidentification is accessible. Furthermore, more is known about the ecology ofcarabids than of almost all other insect families (Thiele 1977; Larochelle 1990).

It is poorly understood how carabids are affected by agricultural disturbance.Previous studies which have attempted to assess consequences of tillage for carabid

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diversity were not conclusive. For example, several studies have shown that carabiddiversity is greater in fields under minimum tillage than conventional tillage(Dritschillo and Wanner 1980; House and All 1981; House and Stinner 1983;

Ferguson and McPherson 1985; Brust et al. 1986; Carcamo 1995; Carcamo et al.1995). Other studies either indicate that tillage does not affect carabids (Tyler andEllis 1979; Dritschillo and Erwin 1982; Hokkanen and Holopainen 1986; Kromp1989; Mack and Buckman 1990; Tonhasca 1993), or even that carabids are morediverse in conventionally tilled fields (Edwards et al. 1979; Barney and Pass 1986).Some researchers also have found that rotation and crop type affected carabid

diversity (Brust et al. 1986), whereas others (Carcamo et al. 1995) did not. Oneproblem with most of these studies is that the only diversity measures that were usedwere richness and abundance. Although these measures have intuitive appeal, theydo not provide information about changes in the relative proportions of species, i.e.,community structure (Magurran 1988). To find out how useful carabid beetles are asindicator species, it is necessary to fully explore their potential with differentmeasures of diversity (see above). Although diversity indices have been used withcarabid beetles on other studies, the beetles need to be examined in the context ofdeviation from log normal distributions of diversity and abundance.

In this study the disturbance we used was tillage. Tillage is the process of turningthe soil and incorporating organic litter into it, and is an important agriculturalpractice. Shifts from high tillage to no tillage practices have been occurringthroughout North America since the 1970s (House and Stinner 1983). The maintypes of tillage used in southern Ontario, Canada are: high (mold board plow),medium (chisel plow) and no (conservation) tillage. In Ontario, no tillage is the leastcommon of the three practices (since the mid-1990s it has been used on only1015% of farms). High tillage penetrates approximately twice as far as mediumtillage. No tillage involves seeding with minimal disturbance (Oryokot et al. 1997).Fields under high and medium tillage, in contrast to those under no tillage, lackdistinguishable soil horizons (Allmaras and Dowdy 1985), have less organic detrituson top (Allmaras and Dowdy 1985), are much drier (House and Stinner 1983;Allmaras and Dowdy 1985), and exhibit more soil compaction (House and Stinner1983; Tonhasca and Stinner 1991). Because increasing tillage decreases the numberof microhabitats available to the beetles and the abundance of their prey (House andParmelee 1985; Stinner et al. 1988), carabid diversity is likely to be affected bytillage. In parallel studies which were conducted on the same sites as the presentstudy, we found that weed diversity was negatively affected by tillage (Murphy etal. 2003) while isopod diversity increased with greater degrees of tillage (Bela-oussoff et al. 1998).

We used carabid beetles which were collected from four farms that represent agradient of tillage disturbance, and then supplemented our findings with a meta-analysis of 45 published data sets. This meta-analysis was an important contributionto the development of our general conclusion about the usefulness of carabid beetlesas indicator species in disturbance. Our null hypothesis for all diversity indices andmodels was that there would be no change with increasing tillage, or otherdisturbance.

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Table 1. Vegetation which was adjacent to all replicates (i.e., fields within farms) used in this study.Mowed margins Wood edge Woods (maplebeech)Daucus carota Poa pratensis Acer saccharumConyza canadensis Phleum pratense Carpinus carolinianaAmaranthus retroflexus Dactylis glomerata Ostrya virginianaAmbrosia artemisiifolia Bromus inermis Viburnum acerfolia

Elytrigia repens Equisetum spp.Alliaria petiolata Erythronium americanum

Claytonia virginianaGeranium robertianum

Materials and methods

Selection of agricultural fields

We began with over 100 willing co-operators within the designated area, and visitedover 30 of them. To ensure that any effects of tillage which we might find wouldreflect the current tillage regime, we required farms on which the same tillagepractice had been in use for a minimum of 5 years. This constraint was most limitingfor the selection of a no tillage farm, because only 1015% of farmers in the GrandRiver Watershed had been using no tillage for at least 5 years before our studycommenced. We wanted to control for as many variables as possible and originallyended up with three farms which were highly comparable except for tillage practice.In the second year of the study we found an additional high tillage farm whichneighboured the no tillage farm. All the fields were to be bordered on one side by awood lot to assure conformity of edge vegetation on at least one side of the field. Wesurveyed the vegetation in the wood lot and field margins surrounding all potentialfields, and we agreed that only fields surrounded by similar vegetation could be usedto assure comparative rigour (Table 1). All of the fields that were used were alsorestricted by having more or less the same rectangular shape (approximately twice aslong as wide) and area (approximately 10 ha). The slopes, aspects and soil types ofall fields also had to be similar; however, one of the high tillage farms had slightlymore loamy soil than the other farms (Table 2). Soils were tested as to type usingmethods described by Spurr and Barnes (1980).

The average corn heat units for each of the farms used ranged between 2650 and2750 (Brown and Bootsma 1993). Crops used included corn (Zea mays L. Pioneer

213902, planted in 76 cm rows at 75000 seeds ha ), soy bean (Glycine max (L.)21Merr. KG 40, planted in 53 cm rows at 50000 seeds ha ) and winterwheat21(Triticum aestivum L., planted in 18 cm rows at 50000 seeds ha ). Farmers applied

21fertilizer (15, 40, 40 kg ha NPK), except in years following G. max. Glyphosate21

was applied at 1.0 kg ai ha as a burn-down herbicide in all rotations and all years.When planting Z. mays, farmers applied pre-emergence tank mixes of linuron and

21metalochlor (1.1 and 1.9 kg ai ha , respectively). For G. max, pre-emergence tank

21mixes of metalochlor and imazethapyr (2.0 and 1.0 kg ai ha , respectively) were

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Table 2. Summary of physical characteristics of the four treatments (farms representing different degreesof tillage) used in this study.Tillage type Location Treatment Replicate Soil type Slope Rotation

No till Oxford NT NT-1 Sandyloam 0.25 3-cropNT-2 Sandyloam 1.50 3-cropNT-3 Sandyloam 1.25 2-cropNT-4 Sandyloam 1.00 3-cropNT-5 Sandyloam 1.25 2-crop

Moldboard Oxford HT1 HT1-1 Sandyloam 1.25 3-cropHT1-2 Sandyloam 0.25 3-cropHT1-3 Sandyloam 1.25 3-crop

Chisel Waterloo MT MT-1 Sandyloam 1.50 3-cropMT-2 Sandyloam 0.75 3-cropMT-3 Sandyloam 1.50 3-cropMT-4 Sandyloam 0.50 3-cropMT-5 Sandyloam 0.50 3-cropMT-6 Sandyloam 0.25 3-crop

Mold board Wellington HT2 HT2-1 Loam 0.00 2-cropHT2-2 Loam 0.00 2-cropHT2-3 Loam 1.75 2-cropHT2-4 Loam 1.75 2-crop

HT1 represents the mold board tillage (i.e., high tillage) treatment in Oxford County and HT2 representsthe mold board tillage treatment in Wellington County. MT is the chisel plowed (i.e., medium tillage)treatment in Waterloo County. NT is the no tillage treatment in Oxford County. Slope is in degrees. Soiltype was determined using the methods of Spurr and Barnes (1980). Three-crop rotation is cornsoybeanwheat, two-crop rotation is cornsoy bean. Replicate refers to field within farms.

used. In T. aestivum, pre-emergence tank mixes of dicamba, MCPA, and mecoprop21

were all applied at 0.5 kg ai ha . The herbicide rates were in accordance withrecommendations in the Ontario Guide to Weed Control (Ontario Weed Committee1996).

We ended up with four farms (with a total of 18 fields representing three differentfarming systems) in Wellington, Waterloo and Oxford Counties, all within 30 km ofone another (Figure 1). With such limited choice, we decided that use of replicatefields within the similar farms would be our experimental design. The farms used inthis study are conventionally (mold board) tilled, chisel plowed or no till. For theremainder of this paper, fields within farms which were tilled with a mold boardplow are called HT (i.e., high tillage), the chisel plowed fields are called MT (i.e.,medium tillage) and the no tillage fields are called NT (i.e., no tillage). In Oxfordcounty, HT1 is adjacent to NT. Other variables are crop type (winter wheat, soybean and corn) and crop rotation (cornsoybean, cornsoybeanwinter wheat). TheHT farm in Oxford County (HT1) is located on Bedford-Blenheim Road, Drumbo.The NT farm in Oxford County (NT) is located on County Road 22, Drumbo. TheMT farm in Waterloo County (MT) is located on County Road 97, Ayr. In 1995, anadditional high tillage cooperator was included (in Oxford) (HT1) whose farm hadthree fields which were within the constraints outlined above, and the HT2cooperator gave us access to more fields, of which only two additional fields whichwere within the constraints outlined above.

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Figure 1. Locations of the four farms which represent different degrees of tillage in southern Ontario.HT1 and NT were in Drumbo, Oxford County; HT2 was in Elora, Wellington County; and MT was inAyr, Waterloo County. See text for descriptions of the farms.

Pitfall traps for the collection of carabid beetles

Carabids were collected from 1994 to 1997. Each replicate (i.e., field) had a transectof seven single pitfall traps. The traps consisted of white plastic tubs which were 8cm deep and 15 cm in diameter. The location of the transect within each replicatewas chosen arbitrarily but began next to the woodlot border, and ran at right anglesto the border. The transect was at least 30 m from the three other edges of the field.For each transect single traps were placed at the crop edge border, 1, 2, 4, 8, 16 and32 m into the crop. Additionally, in 1996 and 1997, two barrier traps were put intoplace into soy and corn replicates to increase sample sizes. The first trap was placed1 m from the crop edge nearest the woodlot and the second trap was placed 32 mfrom the edge. Barrier traps were not put into wheat replicates, because to do sowould have caused much trampling damage around the trap during installation. Thebarrier traps were first tried out in 1996 in NT and HT1 fields, and then put into allnon-wheat replicates in 1997. The two barrier traps were also placed arbitrarilyalong a transect in the replicates, but were separated from the single trap transects byat least 30 m. Barrier traps consisted of 2 m long 3 15 cm high wooden boardswhich were fitted into a cross pattern and sunk about 3 cm into the ground. The endof each board was notched so that a plastic tub could be placed flush against the

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board when the trap was put into the soil. Bamboo poles of 2.5 m height were placedbeside each trap to indicate its location.

Each year, the traps were installed in early June, after corn and soy crops wereplanted and weather permitting. The traps were filled with water and two drops ofdetergent to break surface tension. Once a week all the sites were visited and trapcontents were emptied into 70% ethanol for storage until carabid beetles could beidentified. Because trapping efforts were made while crops were on the replicate,collections were made until mid-August, after wheat was harvested.

Species identification

A key based on Lindroth (19611969) was developed to help with the identificationof the species found in the collection (Belaoussoff 2000). Voucher specimens havebeen deposited in the University of Guelph insect museum.

Diversity statistics

The diversity indices which were calculated include richness, the ShannonWienerindex, evenness, the BergerParker index, the a index, Q-statistic, and the Margalefindex (Taylor et al. 1976; Southwood 1978; Magurran 1988). These indices werecalculated for carabid beetle assemblages collected from replicates in HT1, HT2,MT and NT. Jaccards similarity index was also calculated to determine within-yearsimilarities between and within replicates in the treatments.

ShannonWiener Index (H9) was calculated as:H952O p lnp (1)s di i

where p 5 N /N. N is the number of individuals of the ith species in the sample andi i iN is the total number of individuals.

Shannon evenness was then calculated using the formula:

E5H9 / lnS (2)where H9 is the ShannonWiener index and S is the number of species.

The Margalef index (D) was calculated as:D5 S21 / lnN (3)s d

where S is the number of species and N is the number of individuals.The BergerParker dominance index (d) was calculated from the equation:

d5N /N (4)max twhere N is the number of the most abundant species and N is the total number ofmax tspecies. In order to ensure that the index increases with increasing diversity, thereciprocal form of the measure was used (Magurran 1988).

The Q-statistic (Q), which is a measure of the inter-quartile slope of thecumulative species abundance curve, was calculated first by choosing the upper and

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lower quartiles (i.e., the points below which 25 and 75% of the species abundanceslie) (Kempton and Taylor 1974). Once the quartiles are selected, the Q-statistic wascalculated by:

Q5 0.5n 1On 10.5n / log R /R (5)S D s dR r R 2 11 2where 0.5n is half the number of species in the class in which the lower quartileR 1falls, on is the total number of species between quartiles, 0.5n is half the numberr R 2of species in the class in which the upper quartile falls, and R and R are the1 2number of individuals in the classes with the lower and upper quartiles, respectively.

The a statistic (a) was calculated by:a5N 12x /x (6)s d

where x is determined from S /N 5 [(12x) /x][2ln(12x)], S is the total number ofspecies and N is the total number of individuals.

The above diversity indices are measures of a diversity, which is the diversity ofa biological community within a habitat (Southwood 1978; Magurran 1988). bdiversity is a comparison of species composition between different communities(Southwood 1978; Magurran 1988). To assess b diversity, the Jaccard index (J)was used (Southwood 1978):

J5S / S 1S 2S (7)s dab a b bwhere S represents the number of species shared by community a and communityabb, and S and S are the number of species found in community a and b, respectively.a bDendrograms based on the Sorensen quantitative index were prepared for carabidassemblages between and within treatments (Magurran 1988).

Statistical analysis

Data were collected on the 18 fields (replicates) within four treatments (farms). Thetreatments represented three different tillage types (no tillage, medium, or high).Data from the two high tillage treatments consisted of two farms which were treatedseparately because one of the farms was added a year into the experiment. Eachreplicate was subject to a particular trap type (single or barrier) and crop (corn, soy,or wheat), where the crop type rotated year to year. Data were examined and thedata were found to be approximately normally distributed. Repeated measuresanalysis (RMA) of seven diversity indices (see above) were taken weekly for 9weeks on each replicate at each position along a transect over the course of 4 years(19941997). The SAS procedure PROC MIXED (Littell et al. 1996) was used to fita mixed model where the effect of replicates within treatments was treated as arandom effect, while the other factors, treatment, crop, trap type, and year remainedfixed. All two-way interactions were included as well. F tests (Littell et al. 1996)were used to test the overall effect of the fixed factors (treatments), while individualt-tests were used to reveal differences in the least square means of the levels of eachtreatment.

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In addition to examining diversity index differences between treatments, RMAwas performed on carabid abundance and richness data collected from each replicateover the 9-week sampling period in each year (time). This analysis was done toexamine if there were differences in abundance, or richness, within a given year.First, a linear regression of abundance (or richness) over time was fitted to generate aslope for each treatment combination (treatment, crop, trap type). These slopes weresubsequently analyzed by fitting the model explained above. This analysis wasrepeated using the slopes from regressing abundance (or richness) on the position ofthe traps along the transect in order to examine if there were trends in the abundanceor richness over the different positions of the traps within a given year.

To compare the results of our study with those on other carabid populations,published data sets from various disturbance studies were gathered for meta-

analysis. Disturbances in those studies include: clear cutting (Sustek 1981; Lenski1982), pollution exposure (Critchley 1972; Freitag et al. 1973; Jarosik 1983; Hejkal

1985; Asteraki et al. 1992), forest fragmentation (Niemela et al. 1992; Halme andNiemela 1993) and agriculture (House and All 1981). The same species diversity

indices as used for carabids in this study were applied to those data.

Diversity and abundance models

Carabid data from each treatment were pooled to ensure that the sample sizes wouldbe sufficient to allow for statistically rigorous testing of differences betweendiversity and abundance models. Three diversity and abundance models wereexamined: geometric, log series and log normal. The broken stick model was nottested for, because the data rank abundance graphs indicated that the data did notconform to that model.

Magurran (1988) suggests that the simplest method to assess whether or not ageometric series represents a biological community is to use rank abundance curves.The log series distribution is represented by:

2 3 nax,sax d /2,sax d /3, . . . ax /n (8)s d

2ax is the number of species predicted to have one individual, (ax ) /2 those withtwo and so forth (Fisher et al. 1943; Poole 1974; May 1975). Magurran (1988)outlines the procedure to obtain a as follows. The total number of species, S, isobtained by adding all the terms in the series, which reduces to:

S5a[2ln 12x ] (9)s dwith x being estimated from:

S /N5 12x /x[2ln 12x j (10)s d s dwhere N is the total number of individuals.

Because a and N summarize the distribution and are related by:N5aln 11N /a , (11)s d

the following equation can be used to obtain a :

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a5[N 12x ] /x (12)s d

To determine if our treatment data sets conformed to log normal distributions,they were graduated to a truncated curve using the following general equation(Raukiaer 1918; Preston 1948; Whittaker 1972):

2 2S R 5S exps2a R d (13)s d 0Species abundances were transformed using log to generate intervals of octaves. S2 0is the number of species within the modal abundance octave R . In Equation 13,0S(R) is the number of species in an octave R octaves from R and 1/a is the width of0]the distribution a 5 1/( 2s). This constant, a, for a canonical log normalrelationship has been found to be approximately 0.2 (Hutchinson 1953; Whittaker1972; Colinvaux 1973).

For species distributions that had modes, log normality was tested. A crude modein log normal distributions of diversity and abundance can be seen in histograms asthe value which occurs most frequently but has lower values on either side. Lognormal histograms have modes, but log series graphs do not because the mostfrequent value in those histograms is next to the y-axis, and thus has a lowerneighbouring value only on one side. The Lilliefors test, instead of the Kol-

2mogorovSmirnov x test, was used to indicate whether or not our data was lognormal because it is more conservative in the probability of rejecting the nullhypothesis (Sokal and Rohlf 1995). This statistical test was recommended by ananonymous reviewer of Kevan et al. (1997), and also by W. Matthes-Sears(University of Guelph) and Dr M. Camp (USDA, Maryland), as the most appro-priate way to test for fit to log normal distributions. To detect log series dis-

2tributions, the KolmogorovSmirnov x test was used. As well, rank abundancegraphs and frequency distribution histograms were prepared for pooled carabid datafrom each treatment.

Rank abundance distributions were examined to determine if the findings in ourstudy were similar to those from others [clear cutting (Lenski 1982; Stubblefield et

al. 1993), pollution exposure (Critchley 1972; Freitag et al. 1973; Jarosik 1983;Hejkal 1985; Asteraki et al. 1992), forest fragmentation (Niemela et al. 1992; Halme

and Niemela 1993) and agriculture (House and All 1981)].

Results

General findings

Thirty genera, and 102 species, of carabids were identified from the 20495 carabidbeetles collected during this study. Over four years of collection (19941997) 68species were found on no tillage treatment (NT), 57 on the chisel plowed treatment(MT), and 42 and 53 on the mold board treatment (HT1 and HT2, respectively)(Appendix 1). Species which were found on all four treatments include Anisodac-tylus sanctaecrusis Fabricius, Bembidion inaequale Say, Bembidion tetracolum

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Say, Bembidion quadrimaculatum oppositum Say, Chlaenius tricolor Dejean,Clivina fossor Linne, Harpalus herbivagus Say, Pterostichus melanarius Illiger,

Stenolophus comma Fabricius and Tachys incurvus Say. Agonum muelleri Herbstoccurred frequently on all but NT replicates. Amara Bonelli species were notcollected from HT1, but were found on the other treatments. Cicindela andHarpalus species were far more common on NT than the other treatments.Dyschirus species were not found on NT or the neighbouring HT1, but werecollected in low numbers from HT2 and MT replicates.

There were yearly differences in the ranked order of the most numerous specieswithin each treatment, but the common species within each treatment were presentthroughout the study. For HT1 replicates, species which were common throughoutthe study period were Clivina fossor, Bembidion quadrimaculatum oppositum,Pterostichus melanarius, and Tachys incurvus. For HT2 replicates, the mostcommon species include Agonum muelleri, Pterostichus melanarius, and Bembidionquadrimaculatum oppositum. For MT replicates, the most common species werePterostichus melanarius, and Bembidion quadrimaculatum oppositum. For NTreplicates, the most common species were Harpalus pensylvanicus de Geer,Pterostichus melanarius, Bembidion quadrimaculatum oppositum and Cicindelaformosa generosa Dejean.

Exotic species which were collected from all treatments were: Agonum muelleri,Amara aenea, Bembidion tetracolum, Bembidion quadrimaculatum oppositum,Clivina fossor, Harpalus affinis, Harpalus puncticeps, Pterostichus melanarius,Trechus discus and Trechus quadristriatus (see Spence (1990) for a list of exoticspecies in Canada). The exotic species Bembidion obtusum Serv. was found only onHT1 and HT2, and Amara familiaris Dft. was collected only from NT. The totalpercent abundances of exotic carabids collected from each treatment are: 42.31% inHT1, 77.48% in HT2, 60.59% in MT and 35.87% in NT. The high percentageabundance is because some of the most commonly collected species (i.e., Agonummuelleri, Bembidion quadrimaculatum, Clivina fossor, Pterostichus melanarius) areexotics. The ratio of exotic species to native species collected from each treatmentwas 9:33 in HT1, 10:43 in HT2, 8:49 in MT and 10:58 in NT. The null hypothesisthat exotic species are more common in treatments with less tillage cannot be

2rejected when contingency tables are used (x 5 0.38, df 5 3, P . 0.03).

Carabid richness and abundance over transects

For single trap transects, the results of RMA on carabid abundance reveal that therewere no significant effects of farm, year or crop type per trap, for trap positions ortime (F values ranged from 0.56 to 1.84, df ranged from 13 to 1012). For carabidrichness over time there was no overall significant effect for farms (F 5 2.33, df 53, 12, P . 0.12); however, there was a significant year by crop interaction (F 59.03, df 5 2, 10, P,, 0.01). Least square means reveal that wheat had asignificantly different effect than did corn and soy (t 5 3.03, df 5 10, P , 0.01, andt 5 4.17, df5 10, P,, 0.01, respectively), but that corn and soy did not differ fromone another.

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For barrier traps, the results of RMA on carabid richness revealed that there wasno significant effect of treatment or year for trap positions or time (F values rangedfrom 0.07 to 2.82, df ranged from 13 to 193). Crop type could not be testedbecause barrier traps were only placed in corn and soy fields, and if crop type wasincluded as a factor the model would not converge. Although for carabid abundance,there was no overall significant effect of trap position for either treatment or crop (Fvalues ranged from 0.29 to 0.85, df ranged from 13 and 15), there was asignificant year by time (i.e., collection period) interaction for abundance. Leastsquare means revealed that NT replicates were significantly different from the otherthree treatments in 1997, when more beetles were trapped in barrier traps in NT thanwith the other treatments.

Generally, there was no difference in trap position or over collection time forrichness or abundance of carabids collected in fields of the four treatments.Therefore, it is possible to pool data from the traps over time to calculate diversityindices for each treatment.

Species diversity

Jaccards similarity indexFigure 2 and Table 3 provide Jaccards similarity indices for all replicates. Themean similarity within each treatment is: 0.37 for HT1, 0.46 for HT2, 0.31 for MTand 0.35 for NT. There is high similarity between transect types (i.e. single andbarrier) within replicates, with values ranging from 0.70 to 0.80. More carabids weretrapped in transects with barrier traps than in transects with single traps. However,species compositions found in the two trap types were not significantly different.Dendrograms showed that species composition in NT and MT are more similar toone another than in HT1 and HT2.Within treatments, species tend to be most similarbetween years, but crop type does not seem to have an effect.

Diversity indicesThe overall effect of treatment, crop, or transect, on diversity indices (Q-statistic,Margalef, richness, evenness, ShannonWiener, a and BergerParker) was notsignificant. There were some year by treatment interactions, thus it is necessary tolook at the simple effects of treatment by year comparisons with least squaresmeans. For the Q-statistic, in 1994, HT2 was significantly different from MT andNT (t 5 2.85, df 5 26, P , 0.01; t 5 2.10, df 5 26, P , 0.05, respectively), and, in1996, HT1 was significantly different from NT (t 5 2.11, df 5 26, P , 0.04). Forrichness, in 1996, NT was significantly different from HT1 and MT (t 5 2.14, df 526, P , 0.04; t 5 2.55, df 5 26, P , 0.01, respectively) and, in 1997, NT wassignificantly different from HT2 and MT (t 5 2.74, df 5 26, P , 0.01; t 5 2.28, df5 26, P , 0.03, respectively). For ShannonWiener, in 1994, HT2 was sig-nificantly different from NT (t 5 4.56, df 5 26, P,, 0.01); in 1996, HT1 wassignificantly different from MT and NT (t 5 2.27, df5 26, P , 0.03; t 5 2.28, df526, P , 0.03, respectively). For BergerParker, in 1995, NT was significantlydifferent from HT2 (t 5 4.12, df 5 26, P,, 0.03); in 1996, HT1 was significantly

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Figure 2. Dendrograms of carabid assemblages based on the BrayCurtis index of percent similarity.HT1 and HT2 represent mold board tillage (i.e., high tillage) treatment in Oxford County and WellingtonCounty, respectively. MT is the chisel plowed (i.e., medium tillage) treatment in Waterloo County. NT isthe no tillage treatment in Oxford County. The number after the treatment name refers to replicate field. w5 wheat, s 5 soy, c 5 corn, 94 5 1994, 95 5 1995, 96 5 1996, 97 5 1997.

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Table 3. Jaccards index of similarity. HT1 and HT2 represent the mold board tillage (i.e., high tillage)treatment in Oxford County and Wellington County, respectively. MT is the chisel plowed (i.e., mediumtillage) treatment in Waterloo County. NT is the no tillage treatment in Oxford County. The number afterthe treatment name refers to replicate field. (a) Between treatments in 1994; (b) between treatments in1995; (c) between treatments in 1996; (d) between treatments in 1997.a

NT3 MT1 MT2HT1-1 0.21 0.20 0.33NT3 0.21 0.18MT1 0.21b

HT2-2 HT2-3 HT1-1 HT1-2 HT1-3 NT2 NT5 MT3 MT4HT2-1 0.59 0.59 0.40 0.44 0.35 0.13 0.08 0.37 0.35HT2-2 0.63 0.42 0.47 0.22 0.13 0.14 0.47 0.47HT2-3 0.42 0.40 0.29 0.13 0.14 0.39 0.38HT1-1 0.44 0.44 0.13 0.18 0.18 0.44HT1-2 0.39 0.18 0.11 0.50 0.50HT1-3 0.18 0.17 0.17 0.29NT2 0.23 0.07 0.18NT5 0.09 0.11MT5 0.40c

HT2-2 HT2-3 HT1-1 HT1-2 HT1-3 NT1 NT2 NT3 NT4 NT5 MT1 MT2 MT3 MT4 MT5 MT6HT2-1 0.38 0.32 0.26 0.32 0.28 0.23 0.16 0.33 0.21 0.20 0.30 0.25 0.29 0.31 0.30 0.28HT2-2 0.54 0.41 0.44 0.25 0.26 0.23 0.42 0.28 0.23 0.38 0.33 0.26 0.40 0.38 0.36HT2-3 0.35 0.29 0.31 0.23 0.24 0.24 0.32 0.21 0.37 0.24 0.22 0.33 0.37 0.27HT1-1 0.63 0.39 0.26 0.31 0.29 0.39 0.23 0.27 0.26 0.33 0.53 0.40 0.38HT1-2 0.30 0.26 0.27 0.23 0.32 0.19 0.27 0.39 0.26 0.33 0.27 0.42HT1-3 0.14 0.19 0.24 0.21 0.29 0.29 0.30 0.35 0.83 0.35 0.40NT1 0.48 0.30 0.47 0.29 0.29 0.17 0.14 0.24 0.24 0.19NT2 0.26 0.60 0.47 0.29 0.18 0.20 0.25 0.25 0.21NT3 0.33 0.22 0.20 0.15 0.18 0.36 0.20 0.19NT4 0.40 0.36 0.23 0.17 0.32 0.31 0.26NT5 0.31 0.22 0.25 0.26 0.31 0.30MT1 0.32 0.24 0.39 0.43 0.41MT2 0.25 0.33 0.32 0.36MT3 0.31 0.44 0.42MT4 0.39 0.37MT5 0.63d

HT2-2 HT2-3 HT1-1 HT1-3 NT1 NT2 NT3 NT4 NT5 MT4 MT5 MT6HT2-1 0.43 0.39 0.13 0.39 0.26 0.30 0.24 0.17 0.25 0.33 0.33 0.51HT2-2 0.72 0.11 0.40 0.14 0.19 0.22 0.17 0.18 0.46 0.41 0.37HT2-3 0.12 0.47 0.18 0.22 0.33 0.23 0.25 0.43 0.35 0.38HT1-1 0.21 0.07 0.06 0.24 0.08 0.07 0.14 0.14 0.16HT1-3 0.21 0.22 0.41 0.40 0.46 0.60 0.50 0.49NT1 0.56 0.39 0.39 0.34 0.33 0.36 0.24NT2 0.44 0.48 0.44 0.32 0.33 0.28NT3 0.35 0.55 0.30 0.29 0.36NT4 0.41 0.32 0.28 0.24NT5 0.29 0.33 0.36MT4 0.64 0.41MT5 0.41

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different from MT (t 5 3.32, df5 26, P,, 0.03) and, in 1997, NT was significantlydifferent from MT (t 5 2.89, df 5 26, P , 0.01). For a, in 1994, HT2 wassignificantly different from MT (t 5 2.89, df 5 26, P , 0.01); in 1996, HT1 wassignificantly different from NT (t 5 3.22, df 5 26, P,, 0.03) and, in 1997, HT1was significantly different from MT (t 5 2.28, df 5 26, P , 0.03).

Diversity meta-analysis of published data sets on structure of carabidassemblagesCarabid species diversity was calculated for 45 published carabid data sets from 10studies (Table 4). As with the carabid data collected for this study, no one diversitymeasure was better than the others at pinpointing the sites that were disturbed. Forexample, ShannonWiener (H9) measures for Critchleys (1972) data indicated thatthe site with greatest exposure to pesticide spraying had greater diversity than thecontrol site. Meanwhile, Margalefs index suggested the opposite to be true.Although Lenskis data show that H9 was greater in forested areas than in clear cuts,Margalef, Q-statistic and a did not.

Species abundance distributionsThe species abundance distributions for each farm had long tails of rare species.This is shown in rank abundance graphs (Figure 3) and in frequency distributionhistograms (Figure 4) of pooled carabid species data for each treatment. The carabiddata did not show a mode in the frequency distribution histograms, and thus the veilline could not be estimated. Because there is no mode, the data did not conform tolog normality. All the treatments did not conform statistically to log series dis-

2 2tributions: for HT1 x 5 18.75, df 5 9, P , 0.05; for HT2 x 5 23.30, df 5 10, P2 2

, 0.01; for MT x 5 21.95, df 5 9, P , 0.05; and for NT x 5 33.02, df 5 10,P,, 0.01. The high number of rare species, and the straight line suggested in therank abundance graphs, suggest that all the distribution abundance patterns for thecarabid communities collected are geometric (Magurran 1988). As with the carabidpopulations in this study, all the published carabid species abundance distributionshad long tails of rare species. Because there is no mode, the data did not conform tolog normality. Table 5 lists the log-series analyses for 45 data sets, of which ninewere not log series. In all non-log series data sets there were many rare species,suggesting that they are geometrically distributed (Magurran 1988).

Discussion

Our study joins the studies which suggest that carabid beetle diversity is not affectedby tillage. Unlike those studies, which mainly used only ShannonWiener andspecies richness, we examined a range of different diversity indices, each withdifferent biases and methods of calculation. We wanted to determine if with lesstillage there is decreased dominance (BergerParker), increased evenness (Shannonevenness) or richness (a, Q-statistic, species richness, Margalef, ShannonWiener).

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Table 4. Diversity indices for published carabid data.

Reference Site R A H E BP M Q AlNiemela et al. (1992) Upland aspen forest 18 734 1.39 0.48 0.57 2.58 6.92 3.34

Aspen poplar forest 21 403 1.96 0.64 0.35 3.33 19.90 4.42Spruce bog 13 342 1.81 0.71 0.33 2.06 5.30 4.64Lakeside forest 23 378 2.32 0.74 0.30 3.71 13.30 5.39Meadow 34 454 2.33 0.66 0.33 5.39 12.87 8.51

Halme and Niemela (1993) Contiguous forest 16 437 2.18 0.79 0.23 2.47 6.92 3.26Large fragment 19 194 2.03 0.69 0.26 3.42 9.48 5.22Medium fragment 15 269 1.85 0.68 0.24 2.50 5.31 3.43Small fragment 25 405 2.30 0.71 0.27 4.00 11.01 5.89Edge 32 684 2.39 0.69 0.23 4.75 15.43 6.96Surroundings 54 1720 2.77 0.69 0.27 7.11 23.17 10.35

Sustek (1981) Forest 18 2223 1.88 0.65 0.41 2.21 6.38 2.67Clearing 18 1593 1.93 0.67 0.41 2.31 7.71 2.84

Fisher et al. (1943) Station 1 18 242 2.43 0.84 0.24 3.10 10.06 4.47Station 2 14 302 1.99 0.75 0.29 2.28 9.56 3.04Station 3 17 598 2.20 0.78 0.25 2.50 8.48 3.26Station 4 20 534 2.32 0.77 0.27 3.03 8.45 4.10Station 5 17 984 2.04 0.72 0.37 2.32 12.16 2.92Control 18 700 1.83 0.63 0.37 2.59 18.79 3.37

Critchley (1972) Control 23 524 1.86 0.59 0.50 3.51 19.93 4.92Low dose 23 582 1.93 0.62 0.46 3.46 12.50 4.78High dose 14 114 1.91 0.72 0.39 2.74 11.53 4.19

Lenski (1982) Rms forest 26 1511 2.01 0.62 0.40 3.42 14.95 4.40Rms clearcut 31 1161 2.54 0.74 0.32 4.25 16.14 5.85Er forest 23 301 2.57 0.82 0.23 3.85 12.58 6.07Er clearcut 25 499 2.49 0.77 0.21 3.86 12.49 5.54Rmn forest 19 714 2.18 0.74 0.27 2.74 13.21 3.59Rmn clearcut 18 532 2.09 0.72 0.29 2.71 9.60 3.60

Jarosik (1983) Cernpoll 30 2028 1.63 0.48 0.56 3.81 14.50 4.99Cernunpoll 28 1371 2.09 0.63 0.35 3.74 11.50 4.98Libipoll 35 3872 1.85 0.52 0.58 4.12 13.59 93.38Libiunpoll 30 2710 2.18 0.64 0.26 3.67 10.60 4.72

House and All (1981) Conventional soy 19 2003 1.70 0.58 0.48 2.37 5.56 2.91Conservation soy 22 3081 1.21 0.39 0.69 2.61 9.51 3.20Fescue 21 3240 1.25 0.41 0.67 2.47 9.86 3.01Field 21 7213 0.93 0.31 0.79 2.25 6.52 2.66Woods 19 1622 0.58 0.20 0.72 2.44 8.00 3.40

Hejkal (1985) 1 month 16 347 1.63 0.59 0.40 2.56 14.54 4.942 years 22 416 2.22 0.72 0.35 3.48 9.97 3.486 years 20 1294 2.15 0.72 0.22 2.65 6.39 4.9517 years 21 150 2.63 0.86 0.17 3.99 11.52 3.3622 years 18 75 2.09 0.72 0.35 3.94 14.11 0.34

Asteraki et al. (1992) Control 2 21 481 1.64 0.54 0.50 3.24 24.10 4.48After treatment b2 17 549 1.64 0.58 0.46 2.54 10.92 3.33After treatment r2 12 651 1.51 0.61 0.49 1.70 6.21 2.96

R is richness, A is abundance, H is the ShannonWiener index, E is the Shannon evenness index, BP isthe BergerParker index, Al is the a diversity index, M is the Margalef index, Q is the Q-statistic. Fordescriptions of the sites see the respective studies.

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Figure 3. Rank abundance plots of carabid beetles collected in our study on a logarithmic scale againstthe species rank, in order from the most abundant to the least abundant species. HT1 and HT2 are hightillage treatments, MT is the medium tillage treatment, NT is the no tillage treatment.

Figure 4. Frequency distributions of carabid species collected from treatments representing high tillage(HT1 and HT2), medium tillage (MT) and no tillage (NT), in relation to their abundance.

Even though we examined a range of diversity indices, we found that none of theindexes provided a consistent indication that carabid diversity was different betweencrops or farms. These results are supported by our meta-analysis of publishedcarabid data sets, in which none of the different diversity indices could predict

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2Table 5. Summary of x analysis for log series distributions for data sets in our meta-analysis.

Reference Site R A Log-series Sig

Niemela et al. (1992) Upland aspen forest 18 734 12.95 0.10 . P . 0.05Aspen poplar forest 21 403 7.88 P . 0.05Spruce bog 13 342 9.16 0.10 . P . 0.05Lakeside forest 23 378 1.48 P . 0.95Meadow 34 454 36.34 P , 0.001

Halme and Niemela (1993) Contiguous forest 16 437 1724.00 P . 0.75Large fragment 19 194 11.42 P , 0.05Medium fragment 15 269 5.21 P . 0.75Small fragment 25 405 14.36 0.05. P . 0.025Edge 32 684 11.11 0.10 . P . 0.05Surroundings 54 1720 14.31 0.05. P . 0.025

Sustek (1981) Forest 18 2223 8.10 P . 0.25Clearing 18 1593 9.91 P . 0.25

Fisher et al. (1943) Station 1 18 242 5.96 P . 0.25Station 2 14 302 9.50 0.10 . P . 0.05Station 3 17 598 5.93 P . 0.50Station 4 20 534 3.49 P . 0.75Station 5 17 984 5.28 P . 0.50Control 18 700 16.42 0.025. P . 0.01

Critchley (1972) Control 23 524 12.15 0.10 . P . 0.05Low dose 23 582 19.96 0.025. P . 0.01High dose 14 114 7.40 0.025. P . 0.01

Lenski (1982) Rms forest 26 1511 12.91 0.10 . P . 0.05Rms clearcut 31 1161 12.11 P . 0.25Er forest 23 301 2.33 P . 0.75Er clearcut 25 499 3.27 P . 0.75Rmn forest 19 714 19.11 0.01. P . 0.005Rmn clearcut 18 532 14.01 0.10 . P . 0.05

Jarosik (1983) Cernpoll 30 2028 8.92 P . 0.25Cernunpoll 28 1371 7.91 P . 0.25Libipoll 35 3872 3.53 P . 0.75Libiunpoll 30 2710 6.17 P . 0.50

House and All (1981) Conventional soy 19 2003 6.82 P . 0.25Conservation soy 22 3081 9.54 P . 0.50Fescue 21 3240 11.88 P . 0.25Field 21 7213 6.74 P . 0.75Woods 19 1622 7.60 P . 0.75

Hejkal (1985) 1 month 16 347 11.65 0.25 . P . 0.102 years 22 416 7.37 P . 0.256 years 20 1294 13.51 0.10 . P . 0.0517 years 21 150 14.33 0.05. P . 0.02522 years 18 75 25.89 P,, 0.001

Asteraki et al. (1992) Control 2 21 481 13.04 0.10 . P . 0.05After treatment b2 17 549 5.78 P . 0.50After treatment r2 12 651 3.42 P . 0.75

R is richness and A is abundance. For descriptions of the sites see the respective studies.

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disturbance any more reliably than any of the others. This suggests that diversityindices do not provide reliable tools for detecting disturbance when using assem-blages of carabid beetles as bioindicators.

Indices are commonly used because ecologists consider them as a standardapproach to addressing questions of disturbance. Just because they are commonlyused does not, however, mean that they are reliable. The indices which are used varyfrom study to study, and perhaps the results which researchers use are the ones withwhich the null hypothesis can be rejected (Peters 1991). Because we found thatindices are not reliable measures of disturbance, we tried to use diversity abundancemodels to describe disturbance in our study.

We found that the diversity and abundance of carabid beetle assemblages in ourstudy (Figures 3 and 4), and in published studies (Table 5), are all either log seriesor geometrically distributed. Log normal distribution of diversity and abundance isnot represented in any carabid assemblage, even in undisturbed sites; thus, carabidbeetles cannot be used as indicators of disturbance when deviation from log normaldistribution of diversity and abundance is used. These findings can be explained inpart by carabid beetle sizes.

Because of large size differences among species, it is questionable whethercompetition occurs between many carabid species. Carabid feeding behaviour is

body size dependent (Muller 1985), and between carabid species, size similarity isan indirect measure of likeness in prey preference (den Boer 1980; Brust and House1988). The more than 10-fold size difference from the smallest to the largest beetlespecies strongly suggests that they consume different foods and that they occupyseparate portions of the micro habitat (Thiele 1977). Maximum relative prey sizetends to increase with the body size. Small species (less than 10 mm long) feed onlyon prey with smaller body sizes (Loreau 1988) because they are limited by theirability to catch, or chew, a number of prey types (Hengeveld 1980). In comparison,large carabid species (longer than 10 mm) are not restricted to smaller prey (Loreau1988). As a result, large carabid species tend to have a wider prey spectrum than dosmaller species (Rushton et al. 1991). Additionally, large species generally use adisproportionately wider share of the resources within local ecosystems (Brown andMaurer 1986; Siemann et al. 1996). Thus, although data for Carabidae are wanting,it is reasonable to suggest that larger carabid species catch more, and a greaterdiversity of, prey.

The species assemblages which should be used when measuring disturbance byexamining the degree of deviation away from log normality are functional groups.Carabid beetles, it appears, are not useful as such indicators of disturbance, becausethey are not a single functional group. There is likely to be little interspecificcompetition between carabids because of their size differences, but also becausecarabid feeding behavior is diverse (Larochelle 1990). With the exception of a fewspecialists, most carabid species are opportunistic feeders even though they areoften considered as predators (Thiele 1977). They consume what is available andare not necessarily limited by availability of specific foods (Larochelle 1990).Further, not only are feeding habits of adults diverse, between larvae there are

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additional differences in feeding habits (Lindroth 19611969; Thiele 1977). As aresult of the diversity of feeding behaviours during different life stages of thebeetles, disturbances are not expected to have a uniform effect on all species.Because adults and larvae have different feeding behaviours, there is not necessarilydirect competition between life stages, which in turn suggests that there may not becompetition between all of the carabid species.

Since carabid beetles as a group have feeding behaviors which depend on speciessize and feeding preference, they cannot be considered as a single functional group.Rather, they should be splintered into several size determined groups (i.e., small,medium and large) so that there would be increased likelihood that competitionoccurs within a designated group. To become a complete functional group, each ofthose sub-groups must be united with additional species from other taxa. Forexample, carabid beetles together with staphylinid beetles of similar sizes andfeeding behaviours may make a functional group which can be analyzed for lognormality. Unfortunately this idea is very difficult to test, because staphylinidbeetles are limited by taxonomic factors and the ecology of many of the species isnot well documented.

Log series and geometric distributions are usually considered to result from nichepre-emption (Boswell and Patil 1971; May 1975; Thomas and Shattock 1986;Magurran 1988); however, for carabids this argument cannot be invoked. If niche-preemption occurred, the size of the species which dominate from one year to thenext would be consistent. In this study, and in others, the size of carabid specieswhich dominate from year to year is not consistent (Belaoussoff 2000). If thecarabid assemblage is made up of a number of incomplete functional groups, then alog series distribution is to be expected. Within each size limited sub-group therewould be changes in dominance, and carabids would not necessarily be the taxon towhich the dominant species would belong. By haphazardly categorizing membersfrom different incomplete functional groups together, a log series, or geometric,distribution would be expected.

Carabids are not effective as indicators for a detailed understanding of thedynamics of disturbance within ecosystems, because they are not a functional group.These findings that carabid beetles are not useful as disturbance indicators areimportant, because they lead to hypotheses about the types of organisms which areuseful. Communities which should be targeted for providing potential standardsbased on diversity and abundance models should include species which feed on asingle type of food, species which do not have size dependent feeding, and specieswhich are more or less a complete functional group.

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Appendix 1

Percent abundance of all carabid species collected from various treatments.

Species % Abundance

High tillage treatment HT1 from 19941997Tachys incurvus 39.27Pterostichus melanarius 13.95Clivina fossor 10.52Pterostichus lucublandus 8.25Bembidion quadrimaculatum oppositum 6.64Agonum muelleri 6.53Bembidion tetracolum 3.32Stenolophus comma 2.68Anisodactylus sanctaecrucis 1.08Trechus quadristriatus 0.80Harpalus pensylvanicus 0.77Patrobus longicornis 0.72Loricera pilicornis 0.61Cicindela scutellaris lecontei 0.55Pterostichus mutus 0.53Bembidion rapidum 0.44Agonum cupripenne 0.39Bembidion obtusum 0.36Geopinus incrassatus 0.30Harpalus affinis 0.30Chlaenius tricolor 0.28Agonum placidum 0.17Harpalus herbivagus 0.17Syntomus americanus 0.17Bembidion inaequale 0.11Bembidion rupicola 0.11Harpalus caliginosus 0.11Harpalus puncticeps 0.11Stenolophus lineola 0.11Bembidion versicolor 0.08Trechus discus 0.08Agonum tenue 0.06Bembidion americanum 0.06Colliuris pensylvanica 0.06Harpalus longicollis 0.06Agonum cupreum 0.06Anisodactylus rusticus 0.03Bembidion nitidum 0.03Bembidion salebratum 0.03Chlaenius lithophilus 0.03Omophron americanum 0.03Harpalus faunus 0.03Total number of beetles collected 5 3199High tillage treatment HT2 from 19941997Agonum muelleri 25.13Pterostichus melanarius 18.07Clivina fossor 16.32Bembidion quadrimaculatum oppositum 7.79Loricera pilicornis 5.36

875

Appendix 1. (continued)Species % Abundance

Bembidion versicolor 3.35Bembidion tetracolum 3.29Trechus discus 2.64Pterostichus lucublandus 2.60Bembidion obtusum 2.58Tachys incurvus 2.46Stenolophus comma 1.77Bembidion rapidum 1.29Agonum cupripenne 1.26Trechus quadristriatus 1.23Harpalus herbivagus 0.60Anisodactylus sanctaecrucis 0.55Bembidion salebratum 0.54Chlaenius tricolor 0.41Harpalus puncticeps 0.40Bembidion rupicola 0.37Dyschirius politus 0.28Agonum placidum 0.26Bembidion transversale 0.15Bembidion oberthueri 0.14Agonum tenue 0.12Amara rubrica 0.09Bembidion nitidum 0.09Tetragonoderus intersectus 0.09Amara cupreolata 0.08Anisodactylus nigerrimus 0.08Bembidion transversale 0.08Dyschirius globulosus 0.08Chlaenius lithophilus 0.05Harpalus pensylvanicus 0.05Agonum sordens 0.03Amara aenea 0.03Amara avida 0.03Amara familiaris 0.03Anisodactylus rusticus 0.03Cicindela sexguttata 0.03Harpalus affinis 0.03Agonum darlingtoni 0.02Agonum gratiosum 0.02Agonum hypolithos 0.02Amara littoralis 0.02Dyschirius brevispinus 0.02Dyschirius erythrocerus 0.02Harpalus bicolor 0.02Harpalus caliginosus 0.02Stenolophus lineola 0.02Agonum cupreum 0.02Amara carinata 0.03Total number of beetles collected 5 7328Medium tillage treatment MT from 19941997Bembidion quadrimaculatum oppositum 28.39

876

Appendix 1. (continued)Species % Abundance

Agonum muelleri 14.56Pterostichus melanarius 9.68Clivina fossor 7.18Pterostichus lucublandus 7.15Stenolophus comma 6.56Bembidion inaequale 3.87Agonum placidum 3.75Loricera pilicornis 3.59Dyschirius politus 1.80Anisodactylus sanctaecrucis 1.33Dyschirius globulosus 1.21Agonum cupripenne 1.13Harpalus pensylvanicus 0.90Amara avida 0.86Agonum tenue 0.74Bembidion nitidum 0.62Omophron americanus 0.55Harpalus herbivagus 0.51Pterostichus mutus 0.47Tachys incurvus 0.47Chlaenius tricolor 0.43Bembidion tetracolum 0.39Anisodactylus rusticus 0.35Bembidion obtusum 0.35Bembidion versicolor 0.31Amara aenea 0.27Bembidion rupicola 0.27Harpalus affinis 0.27Amara cupreolata 0.23Dyschirius obtusa 0.23Bembidion americanum 0.20Stenolphus conjunctus 0.16Bembidion salebratum 0.12Bembidion rapidum 0.12Bembidion obscurellum 0.08Stenolophus lineola 0.08Trechus quadristriatus 0.08Agonum gratiosum 0.04Agonum hypolithus 0.04Cicindela formosa generosa 0.04Calasoma calidum 0.04Colliurus pensylvanica 0.04Cicindela punctulata 0.04Cicindela repanda 0.04Cicindela sexguttata 0.04Dyschirius erythrocerus 0.04Dyschirius brevispinus 0.04Harpalus puncticeps 0.04Harpalus erraticus 0.04Harpalus bicolor 0.04

877Appendix 1. (continued)Species % Abundance

Microlestes brevilobus 0.04Stenolophus lecontei 0.04Harpalus faunus 0.04Agonum cupreum 0.04Amara carinata 0.04Bembidion sordidum 0.04Total number of beetles collected 5 3642No tillage treatment NT from 19941997Pterostichus melanarius 21.95Cicindela punctulata 17.51Harpalus pensylvanicus 10.28Anisodactylus sanctaecrucis 7.08Bembidion quadrimaculatum oppositum 5.74Clivina fossor 5.31Pterostichus lucublandus 4.81Cicindela formosa generosa 4.55Harpalus bicolor 3.92Cicindela repanda 3.71Harpalus herbivagus 1.96Pterostichus luctuosus 1.62Cicindela scutellaris lecontei 1.08Stenolophus comma 0.95Harpalus affinis 0.89Anisodactylus rusticus 0.84Amara aenea 0.70Calosoma calidum 0.65Harpalus caliginosus 0.65Harpalus puncticeps 0.65Stenolophus conjunctus 0.58Amara rubrica 0.50Chlaenius tricolor 0.50Bembidion tetracolum 0.48Amara avida 0.34Agonum placidum 0.28Harpalus erraticus 0.21Notiobia picea 0.19Amara cupreolata 0.17Loricera pilicornis 0.17Agonum cupripenne 0.15Agonum muelleri 0.10Amara latior 0.10Calathus ingratus 0.09Microlestes brevilobus 0.09Pterostichus mutus 0.09Agonum sordens 0.07Amara convexa 0.07Stenolophus lineola 0.07Agonum tenue 0.05Bembidion americanum 0.05Bembidion inaequale 0.05Harpalus longicollis 0.05

878

Appendix 1. (continued)Species % Abundance

Omophron americanum 0.05Patrobus longicornis 0.05Tetragonoderus intersectus 0.05Harpalus faunus 0.03Amara familiaris 0.03Amara littoralis 0.03Bembidion nitidum 0.03Bembidion rapidum 0.03Chlaenius lithophilus 0.03Geopinus incrassatus 0.03Harpalus erythropus 0.03Agonum hypolithos 0.02Bembidion obtusum 0.02Bembidion versicolor 0.02Cicindela sexguttata 0.02Colliuris pensylvanica 0.02Dyschirius globulosus 0.02Dyschirius obtusa 0.02Notiobia terminata 0.02Notiophilus aeneus 0.02Stenolophus lecontei 0.02Agonum piccolum 0.02Amara carina 0.02Bembidion sordidum 0.02Harpalus funerius 0.02Total number of beetles collected 5 6326

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