Biodiversity evaluation in agricultural landscapes: above-ground insects

Agriculture Ecosystems fi? Environment ELSEVIER

Agriculture, Ecosystems and Environment 74 (1999) 33-64

Biodiversity evaluation in agricultural landscapes: above-ground insects Peter Duelli *, Martin K. Obrist, Dirk R. Schmatz Swiss Federal Institute for Forest, Snow and Landscape Research, Division of Landscape Ecology, CH-8903 Birmensdorf,

Switzerland

Abstract In agriculture, sustainability can be linked to ecological resilience. In view of present or imminent environmental changes in agricultural landscapes, the diversity of species and genotypes, particularly of potential beneficials and alternative prey, may become of increasing importance. However, the available methods and empirical data concerning species diversity of above-ground insects in agricultural landscapes do not yet allow comprehensive evaluation. Standardized inventory methods must be used more rigorously and over longer time periods to detect significant differences in space and in time. Indicator groups for biodiversity estimates must be defined. Methods for optimizing the reliability and comparability of faunistic inventories are proposed, including rarefaction for reference functions and estimation of species numbers per unit area. Recommendations for optimum sampling periods and average empirical numbers for species diversity and abundance of major arthropod groups are given and compared to published data. In general, organismal biodiversity is higher in less intensely cultivated habitats. Apart from the impact of biocides, variation in species diversity often depends on the biodiversity of the surroundings (mosaic landscape) rather than on differing management regimes. The focus in preserving or enhancing, but also in evaluating biodiversity in cultivated areas thus should clearly be on the landscape level. Structural biodiversity in agricultural areas appears to be correlated with functional and organismal biodiversity of the above-ground insect fauna. ©1999 Elsevier Science B.V. All rights reserved. Keywords: Biodiversity; Evaluation; Arthropods; Insects; Agroecosystems

1. Introduction The debate on whether there is a basic causal rela­ tion between biodiversity and ecosystem stability has never ended since Pimm (1991) published his enlightning review on the topic of 'the balance of nature'. Today there is even less general agreement on the re­ lation between organismal biodiversity (diversity of species and higher taxa; see Harper and Hawksworth (1994) and sustainability. However, there are several * Corresponding author. Tel.: +41-1-739-23-76; fax: +41-1-73922-15 E-mail address: [email protected] (P. Duelli)

examples in which biodiversity can easily be seen as a vital component of sustainability or sustainable de­ velopment (Lovejoy, 1995). The numerous antagonists of pest organisms have made an obvious contribution of ecological and economical importance to agricul­ ture and pest management. The more predatory and parasitoid species and/or genotypes present in a par­ ticular landscape, the higher will be the chances that the effects of sudden environmental changes can be absorbed by ecological resilience, i.e. the ecological communities have a higher capacity to return to equi­ librium population densities (Pimm, 1991). There are three good reasons why surface-dwelling arthropods (mainly carabids and spiders, sometimes

0167-8809/99/$ - see front matter ©1999 Elsevier Science B.V. All rights reserved. ΡΙΙ: S 0 1 6 7 - 8 8 0 9 ( 9 9 ) 0 0 0 2 9 - 8

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staphylinid beetles) are most often used for faunistic inventories in agricultural areas: (1) Most of the species are polyphagous predators and thus the taxonomic groups as a whole are considered as beneficial organisms. (2) All three taxa are easily collected in pitfall traps and thus allow for standardized sampling and comparative interpretation. (3) The catches in most habitat types contain sufficiently high numbers of species and individuals to allow for standard statistical treatment. Moreover, pitfall catches in agricultural habitats rarely contain protected or threatened species. On the other hand, there are also three good reasons why pitfall traps are not always used in biodiversity evaluation: (1) While epigeal predators are excellent indicators for habitat quality in terms of biological control of pest organisms, they make poor correlates for overall organismal biodiversity (Duelli and Obrist, 1998). (2) Biodiversity evaluation in most cases is primarily motivated by nature conservation, therefore, it tends to be focussed on rare, attractive and threatened species rather than on common and inconspicuous beneficials. (3) The efforts and costs for collecting, sorting and identifying epigeal arthropods is often too high compared to inventories of higher plants or birds. Only a reduction and strict standardization of effort and costs will render pitfall trapping more applicable and the results more comparable. Above-ground arthropods in agricultural areas can be used for biodiversity evaluation in three ways: 1. Nature conservation is mainly interested in rare, threatened or even protected species. Inventories must avoid destructive methods, and in many cases even the removal of specimens for reference collections is not possible. Under such circumstances, adequate sampling methods are visual or auditory counts by proven experts, mostly in the form of standardized transects for inventories of butterflies, grasshoppers or dragonflies. The assessment is based on regionally known and agreed conservation values for the identified species (red lists, protected species, faunistic singularities). It is probably realistic to assume that rare and threatened insect species are not playing a major trophic role in agricultural habitats and thus will only have a minimal influence on ecological stability or sustainability. Therefore, the present

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paper will not go into any further detail concerning this conservation-centered group of taxa. 2. Agriculture, forestry and medicine focus on either harmful or beneficial insects. Inventory methods are usually optimized for collecting large numbers of individuals, especially when pest organisms are the target. In addition, in recent years biological control and ecological compensation measures have shifted their attention towards species numbers of potentially beneficial organisms, especially in view of a sustainable management in cultivated landscapes in times of imminent global change. Antagonists of pest organisms therefore, are highly relevant to the topic of this paper. 3. In the last decade biodiversity per se has turned into a very general and basic conservation value (Gaston, 1996). Genetic, organismal and ecological diversity are seen as indicators for environmental quality, especially in areas heavily influenced by human activities such as intense cultivation. The very idea of sustainable development is inherently linked to the concept of biodiversity as advocated at the Rio-Convention in 1992 (Johnson, 1993). Since arthropods comprise most of the organismal variability in practically all habitats, they are good candidates for a quantitative biodiversity evaluation. But not all taxonomic groups in agricultural habitats are equally well suited as unbiased correlates for overall organismal biodiversity at a given site. Considering both effort (costs for collecting and identification) and yield (quantitative correlation with regard to site-specific organismal diversity), three insect taxa seem to perform especially well as biodiversity indicators: the insect order Heteroptera (bugs), and within the order Hymenoptera the Symphyta and Aculeata (Duelli and Obrist, 1998). An excellent way to collect these taxa in a standardized manner is the use of flight traps such as window (interception) traps, Malaise tents and yellow water pans. With sustainable development in view, biodiversity evaluation based on above-ground insects can concentrate on two of the above mentioned methods: polyphagous predatory arthopods collected in pitfall traps, and flight trap samples of biodiversity indicator groups such as the Heteroptera, Symphyta and/or the Aculeata, in combination with vegetational inventories.

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A major problem in reviewing the present knowledge on insect biodiversity evaluation in agricultural areas is the fact that most of the many local inventories are not published in all the details necessary to assess them in a comparative way. Most of this information can only be found in the 'grey' literature of unpublished student theses or in more or less confidential reports to local, regional or federal funding authorities or private enterprises. The aim of the present paper therefore, cannot be to give an account of all the innumerable empirical data on species numbers in habitats of agricultural landscapes, but rather to present a standardized method in order to make those data comparable.

2. Materials and methods 2.7. Sampling

methods

There are numerous ways to collect insects in managed and unmanaged agricultural habitats. The most common inventory methods have been described by Southwood (1978) and in even more detail in German by Janetschek (1982) and Muhlenberg (1993). In order to obtain reproducible results for a scientific evaluation of the species diversity at a particular site, the sampling methods must be strictly standardized. Since the number of species found is positively correlated with the sampling effort, this effort has to be exactly quantified in any inventory. Many elaborate inventory methods have been developed in applied entomology in order to obtain good population estimates for particular species or higher taxa, but for an overall evaluation of organismal diversity only a few trapping methods have been used for standardized inventories. 2.7.7. Pitfall traps Pitfall traps are the best known and most often used inventory method in agroecosystems (see Figs. 1 and 2(b)). They have been widely used to arrive at an indication of habitat quality (Mossakowski and Paje, 1985) and for measuring nature conservation values (Margules and Usher, 1981; Eyre and Rushton, 1989). If we assume random movements of arthropods on a surface, then the probability of an animal to make contact with the border of a circular trap is a linear function

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35

of the trap's diameter, but a multitude of further parameters influence the efficiency of pitfall traps (Luff, 1975; Adis, 1979), which complicates the comparison of data presented by different authors (Topping and Sunderland, 1992). Assuming that the number of individuals caught per cm diameter in any kind of pitfall trap would be at least roughly equivalent, species numbers of various pitfall trap investigations can be compared by a multiplication of trap diameter (cm), number of traps, and the number of weeks the traps were activated. However, during the evaluation of different pitfall trap designs we found funnel traps to be superior to cup traps (Obrist and Duelli, 1996) in that funnel traps are up to three times more efficient per cm trap diameter in catching the following arthropod groups: Carabidae, Staphylinidae, Saltatoria, Diplopoda and Lycosidae (Araneae). Cup traps reach the efficiency of funnel traps only for the Linyphiidae (Araneae) and the Formicidae (Hymenoptera). We recommend the use of funnel traps with a 10-15 cm diameter (Fig. 1) for efficiency of catching, and also for ease of use.

2.7.2. Flight traps Flight interception traps collect insects on the wing more or less randomly. The aim is to obtain a standardized inventory of the 'aereal plankton', as unbiased as possible. The best known methods for biodiversity evaluations are window traps (Figs. 1 and 2(e)) and Malaise tents (Fig. 2(f)). While window traps seem to be preferred for investigations in open landscapes (Chapman and Kinghorn, 1955; Juillet, 1963; Ftirst and Duelli, 1988), Malaise traps are more often used in forests (Townes, 1972; Basset, 1988; Schneider and Duelli, 1997). Yellow water pan traps ('Moerike-traps', Fig. 2(g)) attract flower-visiting insects, but have also been used very successfully for aphids and parasitoid wasps. The exact spectral properties of yellow traps may have an influence on the fauna they attract. Since the human eye perceives yellow differently from the insect eye, the many traps described in publications as all being yellow probably appear quite differently to the insects. The trapping efficiencies of various other colors have been tested for different insect groups in applied entomology, but in the long run, yellow still is the best choice when it comes to producing comparable results.

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Fig. 1. Technical drawings of the trap designs used in this study. The window traps consist of a wooden framework, holding a glass window with a sealed rectangular plastic flowerpot underneath on each side. The pots are loosely placed in the framework and can easily be lifted from the support for weekly emptying of the traps. The yellow water-pans consist of a yellow bucket fixed to a wooden pole, again allowing easy emptying by simply turning the bucket around the axis of the screw. The height of the pan can be adjusted to the height of the growing vegetation, to keep its rim approximately 50 cm above the vegetation. Both types of flight traps are filled with water containing soap to decrease surface tension. Two types of pitfall traps are most commonly used: cup traps (in our case with a diameter of 7 cm and a height of 7 cm), sunk directly into the soil, and funnel traps (in our case with a diameter of 15 cm and a funnel hight of 11cm), set in PVC tubing. A wire mesh (1.5 cm opening) inserted in the end of the funnel prevents the capture of small mammals and amphibians. A transparent roof set on wooden poles approximately 10 cm above the traps provides protection from rain. Traps are filled to 1/3 with a conserving mixture of 2% formaldehyde in water, with soap added to decrease surface tension.

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Fig. 2. (a)-(d) Relative and absolute sampling methods for arthropods on the ground or in the vegetation, (a): Standard sized American sweepnet (38 cm diameter). Sampling units usually are 5 χ 20 sweeps, each sweep covering 180°. (b) Covered funnel trap with 15 cm diameter, for epigeal arthropods, at a distance of at least 10 m from the nearest pitfall trap, (c) and (d) 'Absolute' sampling method for calibrating relative methods. All arthropods larger than 2 mm are collected with an aspirator in a cubic tent with a ground surface of 2 m ; (e)-(h) Relative sampling methods for insects in flight, (e): Window trap to intercept insects in flight. The bird silhouette prevents birds from flying into the glass screen, (f): Malaise trap, in which flying insects are intercepted by a red or black cloth underneath a white tent. Insects crawl up towards the light and end up in a plastic container with alcohol, (g): Yellow water pan, mainly for flower-visiting insects. The height of the pan should always be adjusted so that it is slightly above the vegetation, (h): Combination of yellow water pan (funnel) and window trap, with two plexiglass screens at right angles to avoid the influence of wind direction. This combi-trap can be used in almost all habitat types, even hung up in tree canopies. 2

2.1.3. 'Absolute' sampling methods While the above mentioned collecting methods are clearly 'relative' methods, sampling only a selective part of organismal biodiversity, these methods can be calibrated with the help of 'absolute' collecting meth­ ods. The latter are intended to collect all the organisms present within a given area of a particular habitat type. Examples are various types of suction traps (Hender­ son and Whitaker, 1977; Katz et al., 1989; Muhlen­

berg, 1993); (Fig. 2(c) and (d)), often used in compar­ ison with other inventory methods (Tormala, 1982). To estimate species diversity for a given area such as 1 ha (see Section 2.2.1), the number of individuals present on that area must first be estimated. There are few published data on densities of higher taxonomic units (orders or families) in managed and unmanaged biotopes in agricultural landscapes (Basedow et al., 1991; Elliott et al., 1991; Moreby et al., 1994).

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Fig. 2.

(Continued).

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The present authors extrapolations for species numbers per hectare (Figs. 4-18) are based on 'absolute' suction samples from Katz et al. (1989) and unpublished theses. Since rarefaction curves for managed fields usually show flattened asymptotic extrapolations, the estimations for abundances per hectare are not very critical. Doubling estimated densities from 50000 to 100000 individuals per hectare usually does not increase the estimated number of species considerably. For unmanaged habitats, however, extrapolations tend to increase more steadily, even at higher numbers of individuals. Again, this may reflect reality, considering that with increasing areas of unmanaged land (and hence with increasing numbers of individuals as potential victims falling into the traps), the ecological diversity may increase more than in managed, more uniform areas. In our study, suction samples were taken with a generator-powered aspirator from a surface area of 2 m (Katz et al., 1989) inside a cubic tent placed on the vegetation (Fig. 2(c)). By quickly tilting the cube over the vegetation against the wind, even fast fliers such as syrphids can be trapped. Surface dwelling arthropods were prevented from escaping by hammering the basal metal frame of the cube tent into the soil immediately after placing the tent over the vegetation. To calibrate the method, the procedure of aspirating all arthropods inside the tent was repeated three times in the same place in order to determine the percentage of individuals collected in the first run. At least 8 m (4 plots) were sampled per habitat type between June and August 1987. 2

2

2.2. Data processing 2.2.1. Estimating species diversity with rarefaction curves In contrast to comparing the quantity of caught individuals, comparing species numbers is much more complicated, because the increase in numbers is a non-linear function of the effort of collecting. Rarefaction methods (Simberloff, 1972; James and Rathbun, 1981) can be used to extrapolate these asymptotic functions between numbers of individuals and numbers of species up to an estimated figure for the abundance of an arthropod group per hectare, based on suction trap data as described above (Duelli,

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1997). There are theoretical objections to the extrapolation of rarefaction curves (Achtziger et al., 1992), and accordingly, several simplifying assumptions are included in the procedure presented here. However, as long as there is no better way of estimating species numbers per unit area (such as 1 ha), and there is an obvious need for a rough estimate, it makes sense to use the best empirical methods available. Establishing an average ^//-function (number of species per number of individuals) for a particular group of arthropods and a particular type of habitat requires a large quantity of empirical data. In most cases, at least five independent traps and at least 3 months of collecting turned out to be necessary to reach an asymptotic empirical estimate of the species diversity present in a given biotope. Before it was decided to use the procedure presented here, several mathematical functions were tested for their ability to predict known species numbers for very large samples of carabids and spiders in eight funnel traps in a wheat field, based on smaller samples from five traps in the same field (Duelli et al., unpublished observations).The following function gave the best results: jVsd-exp^pi^ )) 2

=

(l-exp(-piiVf )) 2

where N is ther number of species caught with a given number of traps; N\ represents the number of individuals caught with a given number of traps; N_ and Nj are the total number of species/individuals caught with all traps; p\ and p2 represents function parameters. For reasons of accessability, consistency, security, analytical options, etc., we store all our data on systematics, projects, traps, habitats, inventories, etc. in a major Faunistic Inventory Database with the relational database management system Oracle (FIDO; Schmatz and Obrist, in prepraration). Presently, the large samples of arthropods identified to the species level, collected for several years and for several locations in Switzerland, add up to 162485 data sets, comprising 3583 species and 674221 individuals. This relational data base allows for comparative analysis of temporal, spatial or organismal effects on the numbers of arthropods collected. For a variety of selected inventories we calculated for every number /(i of traps (N represents the total number of traps) the average number of species and individuals s

s

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caught over all possible combinations of i traps. These combinations of numbers of species and individuals were supplemented with the combination '1 species, 1 specimen' and then fitted with a non-linear regression algorithm to the above function. The function is therefore, also fitted through the co­ ordinates of the first specimen collected (one specimen and hence one species) and is forced to go through the coordinates for the total number of all specimens and species collected (e.g. for a selected data sam­ ple of five traps in 28 weeks). The results of the rar­ efaction procedure, i.e., the average number of spec­ imens and species per trap, per two traps, etc., de­ termines the shape of the function between the two given coordinates, 1,1 and N_[, N_ . To reach a more reliable function, we defined rarefaction functions for 2-6 (mean 4.3) different samples (inventories in dif­ ferent years and locations!) and calculated an average function for every combination of habitat and species group. By extrapolating this averaged curve beyond the empirical data it is intuitively assumed that there is an asymptotic approach towards the total number of species present in any given plot. By forcing the curve through the coordinates N_i, N_ and by adding 1, 1 as a data point, the above function was empiri­ cally evaluated as producing the best results regarding this assumption. In a few cases, however, the function still does not seem to reach an asymptote and may even increase steadily to reach unrealistic figures for a particular type of habitat at extremely high extrapo­ lated numbers of individuals (see Section 2.1.3). If es­ timates for species numbers surpass the expected fig­ ures for a whole region, the limitations of the method become obvious, but to a certain extent such a steady increase in numbers of species may have a realistic basis: inventories over longer periods will inevitably collect more species than those present at a particu­ lar moment. Given enough time, many species present in surrounding habitats may eventually also pass by and happen to fall into the traps as a rare stochastic event. s

s

2.2.2. Optimizing effort (collecting, sorting, identification) and yield (percentages of estimated total species numbers collected) Due to financial constraints, most evaluations of faunistic biodiversity in publicly funded programs do

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not allow full season sampling. However, any attempts to minimize the efforts must keep track of the loss of information and the increase in scatter that accompa­ nies a reduction in sampling effort. One of the primary goals of standardisation is the possibility to compare inventories from different in­ vestigations. Minimum criteria must be met with re­ gard to the start and the end of the collecting period in order to include the optimum period proposed in a standardized minimum program. In practice, many investigations start too late and miss the spring peak of species diversity, i.e., the first collecting period in minimum programs with two collecting periods. Another practical problem for comparability is the basic sampling unit (e.g. one trap, one week). To re­ duce costs for field work, people often empty their pitfall traps only after two weeks of sampling. Others reduce the number of killed organisms by activating their traps only every second week. In both cases the results of such investigation are hardly compatible with those of optimized weekly samplings in mini­ mum programs proposed so far (Rumer and Muhlen­ berg, 1988; Duelli et al., 1990; Muhlenberg, 1993). To overcome these discrepancies, we now propose to gen­ erally settle for 2 weeks as the standard sampling unit for data processing with pitfall trap catches. If time allows, it is advisable to set up the traps 1 week be­ fore starting the sampling procedure. In studies using flight traps, however, we recommend weekly empty­ ing and refilling of the traps in order to minimize the problem of drying out of catching solutions. The dates for setting up the traps may vary be­ cause of variations in spring weather in different years. Since arthropods are poikilothermic organ­ isms like all plants, we can use official phenology data of the national meteorological stations to adjust for the optimum number of day-degrees in different years. Experience with meteorological conditions in Switzerland has shown that for a variety of trapping methods (funnel traps, yellow water pans, window in­ terception traps) and for a number of important arthro­ pod groups, the best time to start the first collecting period in spring can be timed in terms of weeks after the full bloom of dandelions (Taraxacum officinale). The link with phenological data not only adjusts for the yearly variation in spring weather, but also for the altitude above sea level in the habitat range of Τ officinale.

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Fig. 3. Evaluation of optimal periods for sampling a maximum proportion of species of a full season catch. All possible combinations of a first 6-week and a second 4-week sampling period were calculated. The first period started 0-8 weeks after full bloom of Taraxacum officinale (wafb7), the second period started 5-20 wafbr (equals pausing 0 to 8-14 weeks before resampling; wbr). The example presented here illustrates the optimal collecting periods for flight traps if all taxonomic groups or habitats are considered.

Species composition changes considerably in most habitats during a season. A temporal reduction into a single sampling period therefore, does not always yield maximum species numbers. The authors use an optimization program to calculate backwards from a full season's collection and determine which combination of dates for two sampling periods would have been best to activate the traps to give the highest yields in terms of species numbers. Technically, an SQL based program extracts from FIDO all combinations of 6 and 4 weeks of pitfall sampling (for flight traps 5 + 5 weeks) from 0 to 20 weeks past the date of full blooming of T. officinale in the year and location where the underlying data

set had been sampled. We further reduce the number of individuals contained in the calculated data sample to 60% by taking only two samples of the first 3 two-week samples (6 weeks collected, 4 weeks analyzed) and only one sample of the second 2 two-week samples (4 weeks sampled, 2 weeks analyzed). Hence the method's name is Opti 4 + 2'. When using flight traps we propose weekly emptying, in contrast to the fortnightly emptying of funnel traps. For flight traps, we take three samples of the first 5 weekly samples (5 weeks sampling, 3 weeks analyzed) and two samples of the second 5 samples (2 out of 5 weeks analyzed), hence the name of the method is 'opti 3 + 2'. The selection of samples is based on the number of individuals found in the subsamples. This procedure

42

P. Duelli et al /Agriculture, Extrapolated catch per ha

Full season catch

Ν sp

65.9 ±38.2

37.2 ±11.1

%sp

177%

100.0% ±29.9%

Araneae Wheat Pitfall traps

Ecosystems and Environment 74 (1999)

Optimal Period Start 2 wafbT Pause 0 wbr

4000

3500

3000

2500

optimal

minimal

6+4 weeks

4+2 weeks

Nsp

31.8 ±8.5

28.6 ±7.5

% sp

86.5% ±5.6%

78.1% ±7.7%

1500

1000

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

31.2 ±7.5

27.8 ±5.9

% sp

85.5% ±9.0%

76.7% ±8.8%

wafbT = weeks after full bloom of Taraxacum

500

0

officinale

500 1000 1500 2000 trapOcnvweeks'Ntraps

2500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

wbr = weeks before resampling

1000

33-64

2000

3000

4000

5000

Ν individuals

Fig. 4. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Frank and Nentwig, 1995).

has been chosen for logics of the temporal course of an inventory. Separation of the first raw samples into taxonomic groups usually takes place while field work is still underway. After separating all samples, the ex­ perimenter can select the jars most filled with individ­ uals for subsequent identification to the species level, i.e., 2 and 1 in case of the 6 + 4 program, 3 and 2 in the 5 + 5 program.

The resulting SQL output listing can be visualized as a histogram or surface plot for identification of peaks in species percentages at certain times of the year (Fig. 3). With this procedure (Opti 4 + 2') for funnel traps a temporal sampling scheme ('start 2 weeks after full bloom of Taraxacum, wait 2 weeks before resam­ pling') is found, which allows a reduction in the effort

P. Duelli et al. /Agriculture,

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Extrapolated catch per ha

Full season catch

Ν sp

69.0 ±26.2

48.0 ±14.9

%sp

144%

Araneae Maize

Ecosystems

4-»-2 weeks

39.0 ±6.9

35.7 ±7.2

%sp

83.4% ±11.1%

75.9% ±8.0%

optimal

minimal

6+4 weeks

44-2 weeks

A

1500

//

1000

500

Ν sp

39.3 ±6.7

35.0 ±7.8

%sp

84.2% ±11.2%

74.2% ±6.7%

i

\

V = i.9753*

L

0

!

500 1000 1500 trap0cm«weeks*Ntraps

2000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

1000

y = 1.J7741* ·

>

Standard Minimum Program 4+2

Pause 2 wbr

· ·

1 2000

6+4 weeks Ν sp

Start 2 wafbT

43

J :

2500 minimal

wafbT = weeks after full bloom of Taraxacum

I

O

3000

Optimal Period

Pause 1 wbr

i

3500

100.0% ±31.1%

optimal

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4000

Pitfall traps

Start 5 wafbT

74 (1999)

2000

3000

4000

5000

Ν individuals Fig. 5. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Hanggi, 1987; Frank and Nentwig, 1995).

for identification to only 37% of the individuals caught within a full season. However, at the same time we register an average of 70% of the species identified in a full season. If we use a general Opti 3 + 2' collecting scheme forflighttraps ('start 3 weeks after full bloom of Taraxacum, wait 1 week before resampling'), we achieve an identical reduction in effort and still col­

lect an average of 49% of a full season's species catch (Figs. 3-18). These numbers are averaged over all taxonomic groups and habitats considered. Concentrat­ ing either on taxonomic group and/or habitat usually results in slightly differing recommendations for col­ lecting, which often result in higher yield/effort ratios (Figs. 3-18).

P. Duelli et al./Agriculture,

44

Extrapolated catch per ha

Full season catch

7000

Nsp

58.7 ±7.1

45.3 ±4.9

6000

% sp

130%

100.0% ±10.9%

Araneae Grassland

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0

Pitfall traps Optimal Period 75 4000

Start 4 wafbT

optimal

minimal

Pause 0 wbr

6+4 weeks

4+2 weeks

;

3

Nsp

35.8 ±6.0

32.5 ±5.6

% sp

78.7% ±5.7%

71.4% ±6.6%

Ο

Ο y = 1.1556x ;<> 2000

!o

Standard Minimum Program 4+2 Start 2 wafbT Pause 2 wbr

optimal

minimal

j 1000

6+4 weeks

4+2 weeks

Nsp

35.5 ±6.5

32.5 ±6.6

%sp

77.8% ±6.7%

71.1% ±7.7%

wafbT = weeks after full bloom of Taraxacum

y 0

>iy = 2.02.02*''

$

.••"'%

ι

*

!·

i

1

500 1000 1500 trap0cm«weeks*Ntraps

2000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

100 Average of 6 rarefaction curves (6 inv entories) ± s.d. ·

90 80 70 60

1 2

;

ο $

40

0

::..:m::::.

30

,—-r •

20

ndividuals

φ

Extrapc I. Ν species per ha: £8.7 ±7.1 (es timated Ν indiv.: 200 D00)

10 0

—

5 n°/ at mnn U /ο α 1 1UUU

r

•

1000

2000

3000

4000

5000

Ν individuals

Fig. 6. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Hanggi, 1987; Luff and Rushton, 1989; Hanggi, 1992).

3. Results and discussion 3.1. Diversity of polyphagous predatory collected in pitfall traps

arthropods

3.1.1. Optimum periods for collecting For reasons of comparability, as explained above (Section 2.2), the standard unit for data processing

with catches from pitfall traps is proposed to be 2 weeks of collecting. Consequently, the recommen­ dations for the previous 'minimum program 3 + 2' (Duelli, 1997) must now be slightly modified for the 'opti 4 + 2' program. In both programs a total of 10 weeks are sampled, 5 + 5 weeks in the aminimum pro­ gram 3 + 2', 6 + 4 weeks in the Opti 4 + 2'. Choosing 4 + 2 = 6 weeks out of a total of 10 weeks yields about

P. Duelli et al /Agriculture,

and Environment 74 (1999)

45

33-64

2000

Extrapolated catch per ha

Full season catch

Nsp

66.7 ±13.4

54.3 ±11.7

% sp

123%

100.0% ±21.5%

Araneae Dry meadow

Ecosystems

1800

Pitfall traps

1600 1400

Optimal Period

, 1200

Start 2 wafbT

optimal

minimal

Pause 4 wbr

6+4 weeks

4+2 weeks

Ν sp

42.3 ±5.0

36.7 ±4.9

%sp

79.0% ±7.7%

68.4% ±7.9%

|

| 1000

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

41.7 ±7.1

36.7 ±4.9

% sp

77.2% ±5.5%

68.4% ±7.9%

wafbT = weeks after full bloom of Taraxacum

: 800 600

Standard Minimum Program 4+2 Start 2 wafbT

0.5168x ! OP

y

400 200 0

2000 4000 6000 trap0cm*weeks*Ntrape

8000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

100

1000

2000

3000

4000

5000

Ν individuals

Fig. 7. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Steinberger, 1986; Steinberger, 1988; Steinberger, 1990; Klinge, 1993; Baur et al., 1996). For spiders in dry meadows, published diversities all appear to be higher than the authors reference data.

20% more individuals compared to 3 + 2 = 5 weeks out of 10 weeks. On the other hand, the timing of the optimum period with the weekly samplings of the 3 + 2 method is more precise, so the number of species collected in 5 instead of 6 weeks is not significantly lower. While the effort for collecting is higher in the 'opti 3 + 2' program (11 instead of six field trips), the

effort for sorting and identification is higher in the 'opti 4 + 2' program (20% more individuals). Con­ sidering the standard deviation introduced by all the other variables, the yield in terms of species numbers can be regarded as equivalent in the two programs. For each of the three most important groups of epigeal arthropods collected in pitfall traps, spiders,

46

Ρ Duelli et al. /Agriculture,

Ecosystems and Environment 74 (1999)

Extrapolated catch per ha

Araneae Wet meadow

Full season catch

3000

2500

Nsp

73.0 ±21.0

60.3 ±17.6

% sp

121%

100.0%. ±29.2%

Pitfall traps

33-64

y k 0.74x ; o-

2000

Optimal Period Start 1 wafbT

optimal

minimal

Pause 1 wbr

6+4 weeks

4+2 weeks

Nsp

51.0 ±14.9

46.7 ±13.4

% sp

84.6% ±2.6%

77.5% ±2.9%

y = 1.5321^ 1000

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

500

Ν sp

49.7 ±12.5

45.3 ±11.9

%sp

83.0% ±5.3%

75.6% ±4.2%

wafbT =s weeks after full bloom of Taraxacum

0

4000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

100 90

1000 2000 3000 trap0cm*weeks«Ntraps

;

i

>

r

Average of 3 ranfaction curves (3 inventories) ± s.d.

80 70 60

o

y

50

:

ο

40

ο

Ο

<>

'" ' · " — —

30 20

W

50% at 540 individuals

10 7 V;;

0 1000

Extrapo I. Ν species per ha: 7 3.0 ±21.0 itimated Ν indiv.: 380 000)

— — ;

2000

3000

1

4000

5000

Ν individuals

Fig. 8. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Hanggi, 1987).

carabids and staphylinid beetles, and for five biotope types with different intensities of agricultural treat­ ments, the optimum periods for collecting were calcu­ lated and are shown on the sheets presented in Section 3.1.2(Figs. 3-18). In the upper left, the optimal period to start collecting in spring is indicated as e.g. 'Start 3 wafbT', which means that the traps have to be acti­ vated 3 weeks after the full bloom of Taraxacum. The

pause before resampling (second period in summer) is indicated e.g. as 'Pause 1 wbr', 'pause for 1 week before resampling'. If one wishes to focus on only one of the three taxa, e.g. carabids or spiders, and only one type of habi­ tat, the figures shown can be used as an Optimal pe­ riod' recommendation for timing the set-up of traps. In many cases, however, two or all three of the taxa

P. Duelli et al /Agriculture,

and Environment 74 (1999)

{

4500 Ν sp

37.9 ±5.6

33.0 ±4.7

%sp

115%

100.0% ±14.2%

ι

4000

Pitfall traps

I

3500 Optimal Period optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Ν sp

30.8 ±4.5

28.6 ±4.9

1 2500 c

% sp

93.3% ±4.2%

86.5% ±6.0%

Ζ 2000

optimal

minimal

6+4 weeks

4+2 weeks

Ν sp

30.6 ±5.1

28.6 ±4.9

% sp

92.5% ±3.4%

86.5% ±6.0%

Pause 2 wbr

wafbT = weeks after full bloom of Taraxacum

ι

y = 1:6295x1 i ----ο

i

3

/ Ϊ

y = 2.80,27x

<

ο

1500

Standard Minimum Program 4+2 Start 2 wafbT

'

« 3000 75

Start 0 wafbT

.

./

^ :

\ J

1000 500 o 0

500 1000 1500 2000 trap0cm«weeks*Ntrape

250C

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

47

33-64

5000

Full season catch

Extrapolated catch per ha

Carabidae Wheat

Ecosystems

• i ; i Average of 5 rarefaction curves (5 inv
60

50

40 ·: 30

0

· ••

'

1

*"

20

10

Iff 50% at 360 ind viduals 1000

• Extrapol. Ν species per ha: 37.9 ±5.6 (estimated Ν indiv.: 80000)

2000

3000

5000

Ν individuals

Fig. 9. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Welling, 1990; Kiss et al., 1993). Since many inventories of Carabidae do not separate all Amara species, the genus is not considered here.

are identified to the species level, and several different types of habitat are sampled. For such cases a 'stan­ dard minimum program 4 + 2' was calculated, which of course is not optimal for all taxa and habitats, but is the best compromise in order to avoid ending up with a very complicated sampling scheme. According to the 'standard minimum program 4 + 2' the first collecting period starts 2 weeks after the

onset of full bloom of dandelions (90% of the plants with open flowers) and lasts for 6 weeks. After a pause of 2 weeks the second period starts and lasts for 4 weeks. These values are calculated from data collected at 400-500 m above sea level. A change of 100 m in altitude usually corresponds to about 1 week for the timing of the collecting period. Thus if the dandelions are in full bloom in the plain and there are no dande-

P. Duelli et al. /Agriculture,

48

Extrapolated catch per ha

Carabidae Maize

Ecosystems and Environment 74 (1999)

Nsp

43.1 ±6.8

40.7 ±7.6

106%

100.0% ±18.8%

Pitfall traps

7000

Full season catch

%sp

33-64

Optimal Period Start 4 wafbT

optimal

minimal

Pause 1 wbr

6+4 weeks

4-1-2 weeks

Nsp

39.0 ±6.6

37.3 ±5.5

%sp

96.1% ±2.0%

92.3% ±3.9%

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Ν sp

38.3 ±6.8

36.7 ±7.0

% sp

94.4% ±2.4%

90.1% ±2.1%

wafbT = weeks after full bloom of Taraxacum

0

officinale

2000 4000 trap0cm*weeks*Ntraps

6000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

wbr = weeks before resampling

Average of 5 rarefaction curves (5 inventories) ± s.d. 60

50

Extrapol. Ν species per ha: 43.1 ±6.8 (estimated Ν indiv.: 50000)

2000

3000

4000

Ν individuals

Fig. 10. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Lovei, 1984; Huber et al., 1987). Since many inventories of Carabidae do not separate all Amara species, the genus is not considered here.

lions in the habitat of interest 200 m uphill, a delay of 2 weeks is advisable.

3.1.2. Empirical and estimated species numbers Six expressions of species numbers (with standard deviations) are given in the charts shown in Figs. 3-18. The full season catch is the number of species col­

lected with a given number of traps (here usually five funnel traps with a diameter of 15 cm) run for an entire vegetative period (in perennial habitats usually from April to September or October). The collecting period in annual crops depends on the management regimes. In rape, wheat and barley fields collecting ends in July immediately before harvesting, while late sowing in maize often delays the start of the 'standard optimal

Ρ Duelli et al/Agriculture, Extrapolated catch per ha

Carabidae Grassland Pitfall traps

Ecosystems

and Environment 74 (1999) 3500

Full season catch

Μ

3000

Ν sp

40.2 ±4.6

34.0 ±5.2

% sp

118%

100.0% ±15.2%

y = o.8152x ; —-.-©--- —

2500 Optimal Period

Start 1 wafbT

optimal

minimal

Pause 9 wbr

6+4 weeks

4+2 weeks

ι

75 2000

Ν sp

29.0 ±5.9

26.5 ±4.5

% sp

84.9% ±5.7%

77.9% ±4.3%

ml

Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

/

Ν sp

28.0 ±5.5

25.0 ±5.0

82.0% ±6.7%

73.3% ±7.1%

wafbT = weeks after full bloom of Taraxacum

.» /·

/

•

Qf Ο

// :

ι

/;

% sp

/ j --

I'j y = i . / f e x }* / ο

Standard Minimum Program 4+2

άο

Γ

ο

1/ 1000 I % I 2000 3000

0

4000

trap0cm*weeks*Ntraps

500C

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

49

33-64

-

Average of 6 rare faction curves (6 inv
60 50 40 30

*>

20

§f/y^S0%&\ 480idividuals i i Extrapol. Ν species per ha: 40.2 ±4.6: (estimated Ν indiv.: 50000)

1000

2000

3000

4000

5000

Ν individuals

Fig. 11. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Huber et al., 1987; Luff and Rushton, 1989). Since many inventories of Carabidae do not separate all Amara species, the genus is not considered here. For Carabidae in grassland, published diversities all appear to be higher than our reference data.

period'. A summary for the full season samples in five different habitats is shown in Table 1. Derived from the full season catches, species numbers for two levels of minimalisation are given in Figs. 3-18: the opti­ mal 6 + 4 weeks and the further selected optimal 4 + 2 weeks. Species numbers are also expressed in percent of the full season catches. A generally slightly lower

yield is reached with the standard minimum program; again the species numbers for both the full 10 weeks of sampling and the selected 6 weeks are given. Yet another expression of species number reported in Figs. 3-18 is the extrapolated number of species per hectare, based on estimated numbers of individuals for that area (see inset of chart with average s/i-functions,

P. Duelli et al/Agriculture,

50

Carabidae Dry meadow

Ecosystems and Environment 74 (1999) 8000

Extrapolated catch per ha

Full season catch

45.2 ±9.7

39.3 ±10.0

115%

100.0% ±25.5%

Ν sp % S p

Pitfall traps

33-64

7000

6000

5000

Optimal Period (0 η

Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

31.5 ±13.6

29.5 ±13.6

%sp

77.2% ±17.5%

71.8% ±19.0%

3

! 4000 "•6 c

y = 1.8642X

3000 ^ Ρ 3.3311* 2000

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

31.5 ±13.6

29.5 ±13.6

77.2% ±17.5%

71.8% ±19.0%

o

i ο

o 0

6

500 1000 1500 2000 trap0cm*weeks*Ntrape

ο 2500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

/

·<

1000

%sp

wafbT = weeks after full bloom of Taraxacum

7

: : i ; Average of 4 rare faction curves (4 inv
60

50

XX ·'.Γ.ϊ.ϊ.

8 40

I

Ζ 30

20

I

5

0% at 160 individu als

ΧΪΧΧ.ΪΧΧ.Χ.Ϊ ; Extrapol. Ν species per ha: 45.2 ±9.7 (estimated Ν indiv.: 30000)

1000

2000

3000

5000

Ν individuals

Fig. 12. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Klinge, 1993; Baur et al., 1996). Since many inventories of Carabidae do not separate all Amara species, the genus is not considered here.

lower right). The relation between the estimated av­ erage species number per hectare and the empirical species number of the full season catch (100%) is given as a percentage in the uppermost left column of the figures. In the upper right corner of the figures, linear func­ tions indicate the number of individuals to be col­ lected with a given number and diameter of pitfall

traps during a given number of weeks of collecting. Filled circles indicate average numbers of individuals (with s.d.) from opti 6 + 4 and 4 + 2 programs, and the open circles indicate those from full season catches. In almost all cases, the efficiency (yield/effort; slope of regression line) is better in the optimal collecting periods. Although the abundances of carabids and spi­ ders can reach higher levels in autumn than in spring,

Ρ Duelli et al/Agriculture,

and Environment 74 (1999)

51

33-64

1800

Extrapolated catch per ha

Full season catch

Nsp

49.5 ±7.7

42.7 ±7.1

% sp

116%

100.0% ±16.6%

Carabidae Wet meadow

Ecosystems

1600

Pitfall traps

1400 1200

Optimal Period Start 1 wafbT

optimal

minimal

Pause 5 wbr

6+4 weeks

4+2 weeks

Nsp

34.7 ±4.5

33.0 ±4.0

%sp

81.9% ±9.2%

78.1% ±9.3%

I

y = 0.4464X i;

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

..'.'Ol-

600 400

^ = 0.551 χ

200

Ν sp

34.3 ±3.5

30.0 ±5.6

%sp

81.4% ±10.5%

70.8% ±11.3%

wafbT = weeks after full bloom of Taraxacum

•P-

: 800

Standard Minimum Program 4+2 Start 2 wafbT

1000

0 0

:

i

i

2500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

500 1000 1500 2000 trap0cnvweeks«Ntraps

:

Average of 3 rare faction curves (3 inv
50

8 40

1

Ζ 30

20

I /%

—

fa/

If

0% at 140 Individuals

10

• Extrapol. Ν species per ha: 49.5 ±7.7; (estimated Ν indiv.: 50000)

i

\

i

:

2000

1r 3000

ι

ι

ι

|-ι

4000

\

! \

Ν individuals

Fig. 13. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Keller, 1974; Huber et al., 1987). Since many inventories of Carabidae do not separate all Amara species, the genus is not considered here.

the periods with maximum species diversity also yield greater numbers of individuals collected per 2 weeks than the fortnightly average of the whole season. The open squares in all graphs represent data from pub­ lished work, cited in the legends. The lower graphs show reference functions for sli-values. Based on rarefaction techniques, all avail­ able data from our Swiss databank FIDO were used to

give reference values for comparison with former and future inventories. The graphs contain information on the estimated species numbers and abundances of in­ dividuals per hectare, and show the empirical values of the opti 4 + 2 programs (solid black dots) of the data sets used for the rarefaction and extrapolation, and the average number of specimens necessary to collect 50% of the estimated number of species on

Ρ Duelli et al /Agriculture,

52

Extrapolated catch per ha

Full season catch

Nsp

41.6 ±9.6

34.4 ±7.8

%sp

121%

100.0% ±22.7%

Staphylinidae Wheat

Ecosystems and Environment 74 (1999)

Pitfall traps

33-64

700 0

600

500

\

y = 0.2565X I J.. ό

I

Optimal Period Start 0 wafbT Pause 2 wbr

optimal

minimal

« 400 y = 0.4208X

6+4 weeks

4+2 weeks

Nsp

31.0 ±7.5

28.6 ±4.9

% sp

89.9% ±3.5%

83.8% ±4.0%

)

: 300 -I

200

7

Standard Minimum Program 4+2 Start 2 wafbT Pause 2 wbr Nsp %sp

optimal

minimal

6+4 weeks

4+2 weeks

100

k

30.4 ±8.2

27.6 ±5.9

87.8% ±6.2%

80.4% ±5.3%

wafbT = weeks after full bloom of Taraxacum

500 1000 1500 2000 trap0cm*weeks*Ntraps

I

:

2500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

ο

!

Average of 5 rare faction curves (5 inv
50

1

4 0

30 ο

J//

5

0% at 140 indi viduals Extrapol. Ν species per ha: 41.6 ±9.6 (estimated Ν indiv.: 50000)

I 800

1200

I

1600

2000

Ν individuals

Fig. 14. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Wittwer, 1993). Since most inventories of Staphylinidae do not separate all species in the subfamily Aleocharinae, all Aleocharinae are neglected here.

that type of habitat. Many of the lower graphs also contain open squares, which represent data sets from published inventories, mostly from Central Europe. 3.1.3. Evaluation The higher the number of specimens collected, and the more independent sets of data from the same type

of habitat that are pooled, the more reliable is the shape of the resulting reference function. For a comparative evaluation such reference functions can be used in dif­ ferent ways. The 'luxurious' way to inventory is to establish a new function for the habitats in question. The shape of the curves and the extrapolated estima­ tion for the total number of species can be compared

P. Duelli et al./Agriculture,

and Environment 74 (1999)

Extrapolated catch per ha

Full season catch

Nsp

37.1 ±3.7

25.5 ±2.1

%sp

145%

100.0% ±8.3%

Staphylinidae Maize

Ecosystems

Pitfall traps

33-64

53

Optimal Period Start 5 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

20.5 ±0.7

18.5 ±0.7

%sp

80.6% ±3.9%

72.7% ±3.3%

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

21.0 ±0.0

18.0 ±1.4

%sp

82.6% ±6.9%

71.1% ±11.5%

wafbT = weeks after full bloom of Taraxacum

'

500 1000 trap0cm*weeks*Ntraps

1500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70 Average of 2 rarefaction curves (2 inventories) ± s.d. 60

50

400

800

1200

1600

2000

Ν individuals

Fig. 15. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). No comparable published data on wetland staphylinids were found. Since most inventories of Staphylinidae do not separate all species in the subfamily Aleocharinae, all Aleocharinae are neglected here.

with the known reference functions and values. The 'economy' way is to use an optimized minimum pro­ gram (e.g. 6 + 4 weeks of sampling, only 4 + 2 weeks sorted and identified), where only the final data point for the numbers of all species and all individuals col­ lected is inserted into the grid chart with the reference function. Rating of species diversity is easily done us­ ing a scale from 1 (lower than standard deviation) to 4

(very high, exceeding standard deviation). Limitation to only four scales allows simple algebraic treatment and visualisation, especially when several taxonomic groups in different biotopes are to be quantified for an evaluation of regional biodiversity. The charts in Figs. 3-18 not only show average functions for various taxonomic 3groups and habi­ tat types, but also the results in terms of numbers of

P. Duelli et al. /Agriculture,

54

Extrapolated catch per ha

Full season catch

Ν sp

65.4 ±22.3

36.2 ±3.5

%sp

181%

100.0% ±9.6%

Staphylinidae Grassland

Ecosystems and Environment 74 (1999)

Pitfall traps

800

700 y =0.1987xi; 600

500

Optimal Period Start 2 wafbT Pause 4 wbr

optimal

minimal

JO (0 3

1

6+4 weeks

4+2 weeks

Ν sp

24.8 ±3.0

20.0 ±2.1

% sp

68.9% ±9.6%

55.3% ±3.1%

Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

400

c

300

200

Standard Minimum Program 4+2

jf* 0,2621 χ

100

Ν sp

24.4 ±4.5

19.6 ±4.0

%sp

67.7% ±12.3%

54.2% ±9.5%

wafbT = weeks after full bloom of Taraxacum

33-64

o 1000 2000 3000 4000 trapOcnrweekS'Ntraps

5000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

Extrapol. Ν species per ha: 65.4 ±22.3 (estimated Ν indiv.: 50000)

800

1200

1600

Ν individuals

Fig. 16. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). Round symbols represent the present authors data, squares are from published work (Wittwer, 1993). Since most inventories of Staphylinidae do not separate all species in the subfamily Aleocharinae, all Aleocharinae are neglected here.

species and individuals of the 'opti 4 + 2 ' program. These also can be used for a comparative evaluation of former or future investigations, as long as the same inventory methods are used. Another potentially very useful type of information can be drawn from the extrapolated rarefaction func­ tion: by drawing a line at 50% of the estimated species

number for 1 ha (or any other unit of surface area),one can determine the number of specimens, which must be collected to find half of the species present on that area. Thus an easy way of minimizing the effort would be to start collecting 2 weeks after the onset of full bloom of dandelions, and to continue collecting only to the point at which the necessary number of speci-

P. Duelli et al. /Agriculture,

and Environment 74 (1999)

Extrapolated catch per ha

Full season catch

Nsp

73.3 ±21.2

35.3 ±8.5

% sp

208%

100.0% ±24.1%

Staphylinidae Dry meadow

Ecosystems

33-64

55

900 800 700

Pitfall traps

600 Optimal Period Start 2 wafbT Pause 0 wbr

JO

optimal

minimal

§ 500

6+4 weeks

4+2 weeks

Ν sp

20.7 ±11.0

18.7 ±10.8

% sp

57.4% ±20.9%

51.2% ±19.5%

">

c 400 300 200

Standard Minimum Program 4+2 Start 2 wafbT Pause 2 wbr

optimal

minimal 4+2 weeks

19.7 ±11.2

17.3 ±10.4

% sp

54.8% ±23.2%

47.8% ±20.4%

o 1000 2000 trapOcrmweekS'Ntraps

:

:

3000

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

70

0.2961 χ

100

6+4 weeks Nsp

wafbT = weeks after full bloom of Taraxacum

y = 0.2212x ; -----,©-

;

:

Λ

Average of 3 rare faction curves (3 inv
;

60

50

I

• /

CO

Ζ 30

5 0% at 520 indiv iduals

20

//φ Extrapol. Ν species per ha: 73.3 ±21.2 (estimated Ν indiv.: 50000)

800

1200

1600

2000

Ν individuals

Fig. 17. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). No comparable published data on wetland staphylinids were found. Since most inventories of Staphylinidae do not separate all species in the subfamily Aleocharinae, all Aleocharinae are neglected here.

mens are collected. With such a crude approach, the number and type of pitfall traps probably would not matter much. After reaching that number of speci­ mens collected, the resulting number of species would simply have to be doubled to arrive at a rough esti­ mate of the total number of species in that area. How­ ever, before this short-cut approach can be seriously proposed, many more empirical data are necessary to

determine how reliable such an estimate would be. Considering the large standard deviation for the 50% values in some of the functions, some caution seems advisable. Minimum programs usually miss out on more than half of the interesting species and there­ fore, are not generally recommended in conservation projects, in which rare species are of particular interest (Schultz, 1995).

P. Duelli et al. /Agriculture,

56

Extrapolated catch per ha

Full season catch

Nsp

70.9 ±48.5

46.3 ±24.8

%sp

153%

100.0% ±53.6%

Staphylinidae Wet meadow

Ecosystems and Environment 74 (1999)

Pitfall traps

33-64

350

300

Optimal Period Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

> 200

Nsp

34.0 ±13.1

27.3 ±10.2

%sp

76.9% ±10.3%

62.1% ±9.1%

150

100

Standard Minimum Program 4+2 Start 2 wafbT

optimal

minimal

Pause 2 wbr

6+4 weeks

4+2 weeks

Nsp

34.0 ±13.1

27.3 ±10.2

%sp

76.9% ±10.3%

62.1% ±9.1%

wafbT = weeks after full bloom of Taraxacum

500 1000 1500 2000 trap0cm»weeks*Ntraps

2500

Filled symbol and line = average catch in 6 and 10 weeks, open symbol and dotted line = full season catch

officinale

wbr = weeks before resampling

Extrapol. Ν species per ha: 70.9 ±48.5 (estimated Ν indiv.: 50000)

800

1200 Ν individuals

Fig. 18. Empirical data for six expressions of species number (upper left, for explanation see text), trapping efficiency (upper right) and reference functions based on rarefaction curves (below). No comparable published data on wetland staphylinids were found. Since the one very diverse natural wetland (over 120 species per ha) contained more than double the number of species found in the two samples (5 years apart) of a seminatural wetland, no average rarefaction function is shown. Since most inventories of Staphylinidae do not separate all species in the subfamily Aleocharinae, all Aleocharinae are neglected here.

3.1.4. Comparison with published data In order to compare published data from different experiments, detailed information on the collecting effort taken is required, including the type, diameter, height and number of pitfall traps used, the type of

preservative agents used, season of sampling, collect­ ing period, altitude, and the exact type of habitat. Un­ fortunately, some of this information is missing from most published reports on organismal biodiversity in agricultural landscapes. Often several different meth-

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and Environment 74 (1999)

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57

Table 1 Summary of Figs. 4-18. Taking together all three taxa, the trend to higher taxonomic diversity with increasing naturalness or lower input becomes obvious for both full season collections and extrapolated estimations for 1 ha Ν species per habitat

Wheat Maize Grassland Dry meadow Wet meadow

All three taxa

Araneae

Carabidae

Staphylinidae

Extrapolated per ha

Full season

Extrapolated per ha

Full season

Extrapolated per ha

Full season

Extrapolated per ha

Full season

145.4 149.2 164.3 185.2 193.4

104.6 ± 2 3 . 6 114.2 ± 2 4 . 7 115.5±13.6 128.9 ± 3 0 . 2 149.3 ± 4 9 . 6

65.9 ± 3 8 . 2 69.0 ± 2 6 . 2 58.7±7.1 66.7 ± 1 3 . 4 73.0±21.0

37.2±11.1 48.0 ± 1 4 . 9 45.3 ± 4 . 9 54.3 ± 1 1 . 7 60.3 ± 1 7 . 6

37.9 43.1 40.2 45.2 49.5

33.0 40.7 34.0 39.3 42.7

41.6 37.1 65.4 73.3 70.9

34.4 25.5 36.2 35.3 46.3

±53.4 ±36.7 ±34.0 ±44.3 ±77.2

ods for collecting have been used, and the contribution of a single method to the published numbers of species and individuals is no longer traceable. Another diffi­ culty stems from transect investigations, where data from different habitat types are pooled to give species numbers. After scanning numerous seemingly rele­ vant papers we came to the conclusion that regional or even 'grey' publications and reports are better suited for our purpose of comparing species numbers than condensed information in scientific journals. Thus, in­ evitably, local results from Central European investi­ gations dominate our preliminary comparison. If one looks at the positions of the open squares in Figs. 3-18, the heterogeneity of the results becomes obvious. Data on Staphylinidae in agricultural habitats are rare, their variability is higher, and the estimated diversities from extrapolations or minimum programs are less reliable. For carabids and spiders, on the other hand, there are many reliable data for comparisons in space and time. All three groups have their highest estimated diversity in seminatural habitats such as wet or dry meadows. Improved grassland scores slightly lower, and uniform and annual crops such as wheat and maize again a bit lower. However, the full season catches show hardly any significant differences in biodiversity among habi­ tat types. This is due to the fact that lycosid spiders and beetles are able to run faster and thus are more likely to be caught in funnel traps on bare ground in inten­ sively managed cropland than when fighting their way through dense vegetation in more diverse habitats or in lush grassland. Since more individuals means more species, the catches in maize, rape, barley and wheat often contain more species than neighbouring seminatural areas. Only data correction with help of the rarefaction function makes the data comparable, and the differences in biodiversity become more obvious.

±5.6 ±6.8 ±4.6 ±9.7 ±7.7

±4.7 ±7.6 ±5.2 ±10.0 ±7.1

±9.6 ±3.7 ±22.3 ±21.2 ±48.5

±7.8 ±2.1 ±3.5 ±8.5 ±24.8

3.2. Diversity of insects collected in flight traps As with the epigeal arthropods treated in the above section, many groups of insects collected in flight traps are also important antagonists of pest organisms in agriculture. Besides their indicative role as beneficials, several groups are considered as quantitative correlates for site specific organismal biodiversity. 3.2.1. Optimum periods for collecting Flight traps such as window traps and yellow wa­ ter pans must be emptied weekly to prevent the water from drying out or putrifying. In most cases it is not advisable to add conserving chemicals (e.g. formalde­ hyde), apart from traces of detergents, because of the risk of poisoning cattle, deer and birds. Therefore, the standard sampling and processing unit for these traps is 1 week, not 2 as with pitfall traps. Based on results of inventories in Switzerland, opti­ mum dates for setting up flight traps for two collecting periods have been established for numerous groups of insects and for the same habitat types as for the pitfall trap data. The results differ much more than in the pit­ fall trap investigations. Therefore, special recommen­ dations ought to be given for the use of window or yellow pan traps, for different taxa and habitats. In or­ der to simplify matters, standard minimum programs for the opti 3 + 2 weeks were calculated for five habi­ tat types, and the efficiency in terms of percentage of species is shown in comparison to the empirical data from full season samples per trap (Table 2). When both trap types, all taxa and all five habitat types are taken together, the optimum periods appear as a cathedral-shaped distribution with two peaks in the range of 50% of species collected (Fig. 3). The sharper spring peak indicates a combination of two

P. Duelli et al. /Agriculture,

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Ecosystems and Environment 74 (1999)

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Table 2 Average number of species collected with one flight trap (window trap or yellow water pan) during the entire vegetation period or with the standard 'minimum program 3 + 2' with only 5 weeks of sampling considered 3

1 window trap

1 yellow pan

Full season

Standard 3 + 2 weeks

Full season

Standard 3 + 2 weeks

All 8 Taxa

Nsp

Nsp

% of full season

Nsp

Nsp

% of full season

Wheat Maize Grassland Dry meadow Wet meadow

61.3±20.8 73.0±102.2 ± 3 6 . 7 202.0±179.6 116.3±50.0

38.5±14.4 52.0±45.2 ± 1 6 . 0 103.0±93.3 61.7±21.3

69.0±22.8 66.7 ± 46.8 ± 1 8 . 9 5 7 . 2 ± 14.9 55.7±16.7

29.4 ±16.3 26.3 ± 8.6 54.5 ± 3 6 . 4 54.2±58.0 44.1±30.9

17.7 ± 1 2 . 2 20.0 ± 5.5 25.4 ± 1 9 . 2 36.8±34.1 27.8 ±20.1

47.3±37.5 65.0±18.8 41.9 ± 2 4 . 3 47.8±26.8 43.3±40.6

All eight higher taxa treated in Tabs. 3-5 are taken together. Only one data set for a window trap in maize was available. Dry meadows are by far the most diverse habitat

a

optimum periods, one starting 3 weeks after the onset of full bloom of dandelions and lasting 5 weeks, the second starting after a pause of only 1 week and also lasting 5 weeks. The broader peak in Fig. 3 indicates a second, equally well-suited combination of two collecting periods of 5 weeks. The first should start 6 weeks after full bloom of dandelions, the second also 1 week after the first. This peak's broad hump shows that the choice of the trapping periods is rather robust in summer. The same graphs prepared separately for the five habitat types summarized in Table 6 clearly demonstrate that the two peaks stem from different habitats. While the first peak is characteristic of insects collected in wheat fields and dry meadows, maize and grassland make up the second peak. Only the wetlands contribute equally to both peaks. Accordingly, the recommendations for flight traps shown in the upper left corner of Tables 3-5 differ markedly from the overall standard opti 3 + 2 program based on the first peak in Fig. 3, which turned out to be slightly higher than the second peak. 3.2.2. Empirical data for species numbers No rarefaction curves are shown here, because in most cases fewer than five traps had been used in the same experimental plot. Instead, species numbers per trap are given. Taking all taxa together, some basic statements can be made. The minimum program 3 + 2 appears to reflect the results of the full season samplings rather well. Species numbers are highest in the dry meadows, followed by the wetlands and the fertilized grasslands. In general, window traps collect more species than yellow pans. Aculeate Hymenoptera, Het-

eroptera and Syrphidae, but also the Staphylinidae well known from the pitfall traps, are enough diverse to be used for biodiversity evaluation. Usually, several traps and perhaps several collecting methods are used, which considerably increases the number of species. However, as long as the inventories are not standardized, a direct comparison of species numbers turns out to be impossible. In a thorough inventory with pitfall, window and yellow pan traps during one full year in 1987, 19 habitats in an agricultural landscape in western Switzerland were sampled with identical effort and identical trapping methods (Duelli and Obrist, 1998). Table 6 summarizes the results for five types of habitats. Clearly the highest biodiversity of higher plants and invertebrate animals was reached in the Mesobrometum, a south-exposed seminatural dry meadow. Two modestly fertilized grasslands (one close to a mixed forest, the other close to the wetland) turned out to be more diverse than the two stations in seminatural wetland. Improved grassland (7 stations) and annual crops (7 stations) showed the lowest diversities in most taxa considered. Thus, while direct comparisons within one simultaneous investigation are possible, comparative evaluations of the results from different inventories are questionable. 3.2.3. Comparison with published data Flight traps sample both the autochthonous fauna and many species which simply pass by on their dispersal flights. Inventories of flight traps therefore, are excellent integrators of regional habitat complexes. However, the problems arising with comparing published results on flight trap catches are even more pronounced than with pitfall traps. Inventory methods

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59

Table 3 Average number of species collected in grassland with one flight trap (window trap or yellow water pan) during the entire vegetation period or with the standard 'minimum program 3 + 2' with only 5 weeks of sampling considered 3

Grassland

1 windows trap

1 yellow pan

Optimal period: start 8 wafb T; pause 2 wbr

Full season

Standard 3 + 2 weeks

Full season

Standard 3 + 2 weeks

TAXA

Nsp

Nsp

% of full season

Nsp

Nsp

% of full season

Hym. Aculeata Hym. Symphyta Heteroptera Carabidae Staphylinidae Coccinellidae Neuroptera Syrphidae

25.3 ± 1 2 . 7 4.2 ± 2 . 3 16.4 ± 6 . 9 11.5±2.6 35.6 ± 7 . 2 3.2±2.1 3.0±1.1 3.0 ± 2 . 0

9.5 ± 4 . 7 1.2±0.9 7.7 ± 2 . 4 4.6±1.9 17.5 ± 2 . 8 1.6±1.3 1.5 ± 0 . 7 1.6±1.4

38.8 26.9 49.0 40.3 50.2 54.8 50.2 64.1

24.0 10.5 3.5 0.5 2.1 0.8 1.0 12.1

10.3±7.1 5.1 ± 4 . 1 1.7 ± 1.1 0.3 ± 0 . 8 1.1 ± 2 . 0 0.6 ± 0 . 8 1.1 ± 0 . 4 5.1 ± 2 . 9

41.6 35.5 43.5 9.5 18.8 38.1 100.0 48.2

±10.1 ±23.8 ±12.4 ±13.6 ±8.7 ±35.0 ±13.2 ±7.6

±13.3 ±7.8 ±1.9 ±1.0 ±3.5 ±1.0 ±0.4 ±7.6

±14.5 ±25.1 ±28.4 ±25.2 ±32.2 ±48.8 ±0.0 ±20.2

The optimal collecting periods differ markedly from the standard recommendation for flight traps with a very late start of the first period. Table 4 Average number of species collected in seminatural habitats with one flight trap (window trap or yellow water pan) during the entire vegetation period or with the standard 'minimum program 3 + 2' with only 5 weeks of sampling considered 3

1 yellow pan

1 window trap

Full season

Standard 3 + 2 weeks

% of full season

Nsp

Nsp

% of full season

Dry meadow optimal period: start 1 wafbT; pause 14 wbr 35.5 ± 4 3 . 1 74.0 ± 8 0 . 6 Hym. Aculeata 6.0 ± 8 . 5 10.0±14.1 Hym. Symphyta 19.0 ± 1 8 . 4 39.0 ± 3 5 . 4 Heteroptera 5.0±1.4 14.5 ± 0 . 7 Carabidae 25.5 ± 1 6 . 3 46.5 ± 3 6 . 1 Staphylinidae 4.5 ± 0 . 7 4.5 ± 0 . 7 Coccinellidae 2.5 ± 2 . 1 3.0 ± 2 . 8 Neuroptera 5.0 ± 2 . 8 10.5 ± 9 . 2 Syrphidae

39.9 ± 1 4 . 8 30.0 ± 4 2 . 4 46.4 ± 5 . 1 34.3 ± 8 . 1 59.0 ±10.8 100.0 ± 0 . 0 90.0±14.1 58.1 ± 2 3 . 9

30.8 ± 3 1 . 9 4.4 ± 9.3 5.2 ± 4 . 8 0.6 ± 0.9 3.4 ± 2.1 2.2 ± 0.8 0.9 ± 0.3 6.7 ± 7.8

2 1 . 0 ± 18.1 3.3 ± 5 . 8 4.0 ± 2 . 4 0.0 ± 0 . 0 1.8 zb 1.9 1.3±0.8 0.8 ± 0 . 4 4.5 ± 4 . 7

58.5 16.1 48.8 0.0 38.9 69.5 83.3 67.6

±12.8 ±25.2 ±26.6 ±0.0 ±38.0 ±40.0 ±40.8 ±30.8

Wet meadow optimal period: start 3 wafbT; pause 5 wbr 18.3±2.1 36.0 ± 1 1 . 0 Hym. Aculeata 1.3 ± 1.5 4.0 ± 4 . 0 Hym. Symphyta 9.7 ± 5 . 5 22.7 ±13.3 Heteroptera 8.0±3.0 12.7±6.1 Carabidae 15.0 ± 4 . 6 26.3 ± 7 . 2 Staphylinidae 2.3 ± 2 . 5 4.0 ± 2 . 6 Coccinellidae 2.3 ± 0 . 6 2.7 ± 1 . 2 Neuroptera 4.7 ± 1 . 5 8.0 ± 4 . 6 Syrphidae

53.1 ± 1 0 . 7 20.8 ± 1 9 . 1 42.9 ± 0 . 8 67.2 ± 1 4 . 1 57.4 ± 9 . 5 46.0 ± 3 9 . 9 91.7±14.4 66.4 ± 2 4 . 8

19.3±5.5 3.1 ± 6 . 5 3.5 ± 3 . 8 0.9 ± 1 . 3 5.3 ± 3 . 7 1.8 db 1.1 0.8 ± 0 . 4 9.4 ± 8 . 5

10.3 ± 1 . 9 3.8 ± 4 . 5 1.5±1.3 0.3 ± 0 . 5 2.5 ± 3 . 3 1.0±1.2 0.5 ± 0 . 6 8.0 ± 6 . 9

48.3 24.1 42.9 25.0 38.2 50.0 50.0 68.0

±18.4 ±28.6 ±42.1 ±50.0 ±44.1 ±57.7 ±57.7 ±26.5

TAXA

Full season

Standard 3 + 2 weeks

Nsp

Nsp

The optimal collecting periods in dry meadows differ markedly from the standard recommendation for flight traps with a very late start of the second period. Aculeate Hymenoptera, Staphylinidae and Heteroptera clearly dominate the catches in flight traps (since Diptera and Homoptera are not treated here). a

vary even more widely, and the published information on the efforts taken is even less quantifiable. The best evaluation of biodiversity at this stage of knowledge comes from direct comparisons of species diversities in simultaneous investigations with identical methods.

Orchards have been sampled for organismal biodiversity in various countries. In a comparison between peach orchards with organic, integrated or conventional management it was found that maintenance of a higher biodiversity depends on both the reduced use of pesticides and the presence of a permanent weed

P. Duelli et al /Agriculture,

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Table 5 Average number of species collected in annual crops with one flight trap (window trap or yellow water pan) during the entire vegetation period or with the standard 'minimum program 3 + 2' with only 5 weeks of sampling considered 3

1 window trap

TAXA

1 yellow pan

Full season

Standard 3 + 2 weeks

Full season

Standard 3 + 2 weeks

Nsp

Nsp

Nsp

Nsp

% of full season

Wheat optimal period: start 1 wafbT; pause 3 wbr 7.8 ± 2 . 4 Hym. Aculeata 11.8±4.7 Hym. Symphyta 2.4 ± 2 . 4 1.3 ± 1.3 Heteroptera 9.5 ± 2 . 6 6.5 ± 2 . 3 Carabidae 6.8 ± 2 . 1 4.4 ± 2 . 0 Staphylinidae 25.9 ± 6 . 0 14.3 ± 3 . 9 Coccinellidae 2.4 ± 1 . 7 2.0 ± 1 . 6 Neuroptera 1.1 ± 0 . 4 1.3±0.5 Syrphidae 1.4 ± 0 . 9 1.3 ± 0 . 7 Maize optimal period: start 5 wafbT, pause 1 wbr Hym. Aculeata 18.0 ± 9.0 ± Hym. Symphyta Heteroptera 12.0±10.0 ± Carabidae 9.0 ± 7.0 ± Staphylinidae 20.0 ± 16.0±Coccinellidae 4.0 ± 4.0 ± Neuroptera 3.0 ± 3.0 ± Syrphidae 7.0 ± 3.0±a

68.7 43.8 68.2 65.5 55.0 74.0 93.8 83.3

±10.6 ±41.7 ±11.7 ±23.2 ±6.8 ±35.5 ±17.7 ±35.6

50.0 ± 83.3 ± 77.8 ± 80.0 ± 100.0 ± 100.0 ± 42.9 ±

-

% of full season

±4.8 ±3.8 ±1.0 ±0.5 ±2.7 ± 1.3 ±0.4 ±1.7

9.3±4.1 3.0 ± 2 . 7 0.8 ± 0 . 4 0.5 ± 0 . 8 1.2±1.8 0.5 ± 0 . 8 1.0 ± 0 . 6 1.3±0.8

49.4 ± 1 6 . 5 44.1 ± 3 6 . 0 45.8 ± 3 3 . 2 33.3±51.6 19.5 ± 3 0 . 6 33.3±51.6 83.3 ± 4 0 . 8 69.5 ± 4 0 . 0

6.7 ± 1 . 5

5.3 ± 1 . 2

80.2 ± 5 . 4

3.0 ± 2 . 6 2.3 ± 1 . 2 5.0±1.7 2.0 ± 0 . 0 2.0 ± 1 . 0 5.3 ± 0 . 6

2.3 ± 2 . 1 1.3±0.6 4.3 ± 1 . 2 2.0 ± 0 . 0 1.0 ± 0 . 0 3.7 ± 0 . 6

16.4 2.7 2.2 0.2 2.8 1.3 0.9 2.7

53.3 66.7 88.9 100.0 61.1 70.0

±50.3 ±33.3 ±9.6 ±0.0 ±34.7 ±17.3

Only one data set for a window trap in maize was available.

cover (Paoletti et al., 1993). The differences between treatments were much more pronounced in the results from sweep net and beating tray samples than from pitfall traps. A comparison of the above-ground insects in uniform versus mosaic landscapes in Poland, Romania, Russia and Italy revealed the highest insect biomasses in meadows, the lowest in spring cereal crops (Ryszkowski et al., 1991). When comparing insects in the same crops, but with different surroundings, the fields in diversified landscapes had much higher abundances. In a comparison of the entomofauna in cultivated fields, fallows and meadows with Malaise traps, Greiler and Tscharntke (1993) found increasing abundances but decreasing diversities with an increase in cultivation intensity. A similar result was reported in a study of carabid beetles in Italian apple orchards (Paoletti et al., 1995). In reviewing the highly variable data on insect diversity in Hungarian apple orchards, Szentkiralyi and Kozar (1991) were able to state that the lower the intensity of management and the higher the diversity of the surrounding vegetation, the higher was the species diversity in an orchard. Furthermore, more than 50% of the species collected must have arrived from surrounding habitats.

The diversity of Heteroptera in Swiss apple orchards was significantly higher in less intensely managed orchards, and the family Miridae was almost completely absent in intensely managed orchards (Schaub et al., 1987). An enormous number of insects were collected in Hungarian apple orchards under various management intensities in the course of 5 years, using a large variety of inventory methods (Meszaros, 1984b). Considering a total of 1759 identified arthropod species, the strongest influence on biodiversity was contributed by the intensity of chemical treatment, followed by the plant species diversity in the orchard and the surroundings. In an equally extensive survey on maize fields in Hungary (Meszaros, 1984a), crop rotation was compared to monocultural management. More than twice as many species were found in the monoculture, although the field was 400 ha instead of only 20-100 ha in the crop rotation fields. The unexpectedly high number of species found in the monoculture can be explained by its location adjacent to a neglected park, while the rotation fields were surrounded by intense agriculture. On the other hand, focussing on the diversity of Diptera in arable farming systems in Germany, Heynen

P. Duelli et al /Agriculture,

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61

Table 6 Comparison of the results of simultaneous standardized sampling in 19 trapping stations in western Switzerland during one full year (1987)

a

Nsp nind

Total

Dry meadow

Wetland

Seminatural meadow

Improved grassland

Wheat maize

Araneae

169 49462 824 68281 98 31412 15 142 204 15957 19 1647 440 34743 64 2974 68 3336 118 2138 338 23030 32 12885 202 7824 102 2319 13 6261 16 420 31 260 19 1208 32 3729

90 1711 352 6747 38 318 5 44 92 2540 8 631 258 3973 42 684 35 522 68 436 214 4456 26 1558 147 2781 40 116 4 412 5 23 18 47 9 87 17 145

81 1149 223 1573 46 340 3 3 67 461 9 50 131 1891 19 101 30 355 25 214 92 1650 15 1193 53 387 24 71 8 1790 3 7 6 8 10 63 14 244

54 1453 263 4771 42 2192 4 8 74 958 8 234 154 2311 24 267 24 236 35 88 107 2483 18 1630 62 619 27 234 5 246 4 45 8 14 12 150 15 149

36 3927 187 3519 34 1666 3 4 49 859 4 36 103 1872 17 154 15 180 15 56 65 877 13 412 36 275 16 190 2 268 3 26 6 11 6 81 13 256

30 2151 163 3459 32 2053 3 7 45 652 3 33 106 1323 19 68 8 53 16 101 59 596 12 399 34 158 12 38 1 43 2 16 7 14 3 18 12 143

221 191214

1096 18678

633 8640

703 11946

456 10887

413 7879

Coleoptera Carabidae Coccinellidae Staphylinidae Diplopoda Diptera Empidoidea Syrphidae Heteroptera Hymenoptera Formicidae Aculeata excl Formicidae Symphyta Isopoda Neuroptera Psocoptera Saltatoria Thysanoptera Total

Numbers of species (upper numbers) and individuals (lower numbers) per trap site for all taxa completely or partly identified at the species level. Arthropod diversity is highly correlated with numbers of flowering plant species (Duelli and Obrist, 1998).

a

(1990) found no significant differences in 10 dipteran families between integrated and conventional farming. Similarly, Moreby (1996) found little difference in the species diversity of Heteroptera in organic and conventional wheat fields. Sengonca et al. (1986) compared spider diversities in standard tree apple orchards and low tree orchards in Germany, all without chemical treatment. An average of 22 species were found

in low tree stands, whereas the standard tree orchards yielded an average of only 15 spider species.

4. Conclusions The main conclusion of the present survey on the available methods and empirical data concern-

62

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Ecosystems and Environment 74 (1999)

ing species diversity of above-ground insects in agricultural landscapes must be that the scientific levels of both methods and data do not yet allow for a comprehensive evaluation of the correlation between organismal biodiversity and sustainability. Standardized inventory methods must be applied more rigorously and over longer time periods to detect significant differences in space and in time. Indicator groups for biodiversity estimates need to be defined. They should correlate well with overall organismal biodiversity. Their optimum period for collecting should either be broad, allowing for an ar­ bitrary setting of the collecting period. Alternatively, if the optimum period is short and clearly defined, rigid control of the temporal setting of traps becomes mandatory. With regard to biological control, sustainability can be linked to ecological resilience in agricultural land­ scapes. At first sight, for fighting pest outbreaks, the abundance of antagonist individuals (predators and parasitoids) might be far more relevant for ecologi­ cal resilience than their species diversity. However, in view of present or imminent environmental changes in agricultural landscapes, the diversity of species and genotypes of potential beneficials and alternative prey may become of increasing importance. Another important conclusion is that there is an ob­ vious limitation to minimalizing the efforts in the sense that the reliability of an evaluation of biodiversity can­ not be based on single taxa, because they might not be correlated well with overall biodiversity, or on an excessively brief collecting period, because the scatter will outweigh the signal. Faunistic inventories of the organismal biodiversity of above-ground insects and spiders inevitably are far more expensive and tedious than inventories of higher plants or birds. At present there is no general agreement on which taxa are suf­ ficiently correlated to overall organismal diversity to make them good indicators for biodiversity. The conclusion from direct comparisons of simul­ taneous inventories within the same research project is that, in general, organismal biodiversity is higher in less intensely treated habitats. Apart from the obvious impact of biocides of all kinds, this higher species di­ versity is often caused by the biodiversity of the sur­ roundings (mosaic landscape) rather than by differing management regimes. The focus in preserving or en­ hancing biodiversity in cultivated areas thus should

33-64

clearly be on the landscape level. Structural biodiver­ sity (Noss, 1990) in agricultural areas appears to be correlated or even causally linked to organismal and functional biodiversity.

Acknowledgements The authors are very grateful to all the many spe­ cialists who identified enormous numbers of speci­ mens, to numerous students who collected and sorted the material, and to Karin Loeffel for scanning both published and 'grey' literature. Many thanks to Michel Studer and Maria Freeh, who were strongly involved in the development of rarefaction functions and pro­ grams for optimum sampling periods. Financial sup­ port by the Swiss National Science Foundation and the Swiss Federal Office of Environment, Forest and Landscape is acknowledged.

References Achtziger, R., Nigmann, U., Zwolfer, H., 1992. Rarefaction-Methoden und ihre Einsatzmsglichkeiten bei der zoookologischen Zustandsanalyse und Bewertung von Biotopen. Z. okol. Nat.Schutz 1, 89-105. Adis, J., 1979. Problems of interpreting arthropod sampling with pitfall traps. Zool. Anz. Jena. 202, 177-184. Basedow, T., Braun, C , Luehr, Α., Naumann, J., Norgall, T., Yanes, G.Y., 1991. Abundance, biomass, biomass and species number of epigeal predatory arthropods in fields of winter wheat and beets at different levels of intensity: Differences and their reasons: Results of a study at three intensity levels in Hesse (Germany) 1985-1988. Zool. Jb. Syst. Oekol. Geogr. Tiere 118, 87-116. Basset, Y., 1988. A composite interception trap for sampling arthropods in tree canopies. J. Aust. Entomol. Soc. 27, 213-219. Baur, B., Joshi, J., Schmid, B., Honggi, Α., Borcard, D., Stary, J., Pedroli-Christen, Α., Thommen, G.H., Luka, H., Rusterholz, H.-.R, Oggier, P., Ledergerber, S., Erhard, Α., 1996. Variation in species richness of plants and diverse groups of invertebrates in three calcareous grasslands of the Swiss Jura mountains. Rev. Suisse Zool. 103, 801-833. Chapman, P., Kinghorn, J., 1955. Window flight traps for insects. Can. Entomol. 87, 46-47. Duelli, P., 1997. Biodiversity evaluation in agricultural landscapes: an approach at two different scales. Agric. Ecosyst. Environ. 62, 81-91. Duelli, P., Obrist, M.K., 1998. In search of the best correlates for local organismal biodiversity in cultivated areas. Biodivers. Conserv. 7, 297-309.

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