From multi-criteria approach to simple protocol: Assessing habitat patches for conservation value using species rarity

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From multi-criteria approach to simple protocol: Assessing habitat patches for conservation value using species rarity Ferenc Samua,*, Pe´ter Csontosb, Csaba Szineta´rc a

Plant Protection Institute, Hungarian Academy of Sciences, P.O. Box 102, Budapest H-1525, Hungary MTA-ELTE Research Group in Theoretical Biology and Ecology, Pa´zma´ny P. stny. 1/c, Budapest H-1117, Hungary c Berzsenyi College, P.O. Box 170, Szombathely H-9701, Hungary b

A R T I C L E I N F O

A B S T R A C T

Article history:

We investigated conservation value (CV) related to the quality of spider communities in dif-

Received 5 September 2007

ferent non-wooded habitat patches – ranging from arable land to natural grasslands. The

Received in revised form

study was conducted in two ecologically distinct regions of Hungary: the Hungarian Great

6 March 2008

Plain and the Buda Hills. We used seven variables to indicate CV which together formed a

Accepted 10 March 2008

multi-criteria space of spider community characteristics. These variables were either

Available online 5 May 2008

related to species characters obtained from an extensive background database: abundance and frequency based rarity, specialist status, association to natural habitats; or were

Keywords:

calculated for the community at the given patch: species richness, functional diversity

Habitat evaluation

and species evenness. Using the variables in an ordination analysis we could establish a

Prioritization

gradient of the patches in the multi-criteria space of the spider community characteristics.

Spider community

Position of patches along the first axis of the ordination was taken as the multi-criteria

Araneae

measure of CV. CVs established this way were strongly and positively correlated with an

Rare species

independent botanical CV assessment. We also sought a simpler measure of spider CV

Specialist status

by: (a) calculating only one variable out of the seven and using it as a surrogate for the

Grassland

multi-criteria CV measurement; by (b) calculating this variable only for a short time period

Landscape mosaic

or (c) for only one spider family. Average abundance based rarity value of the species proved

Species character database

to be the best surrogate of the multi-criteria CV measure for both regions, and it also performed very well when sample size was restricted to two sampling occasions per patch or to a single family. This adds further evidence to, what has been found in other studies, that species rarity is a sensitive and reliable measure of the ecological and conservational status of communities. Ó 2008 Elsevier Ltd. All rights reserved.

1.

Introduction

In Europe, where the landscape has been used and modified by the human population for well over a thousand years, natural grassland areas are scarce and where present are usually embedded in a mosaic of other habitats (Zo´lyomi, 1942; Fekete et al., 2000). Since natural areas are not of equal value for nature conservation and landscape changes take place quite

rapidly, there is a constant need for the evaluation, re-evaluation and prioritization of the natural, close to natural, and even highly modified habitat patches for conservational purposes. Assessing the conservation value (CV) of a habitat patch may help to determine whether the area should or should not be protected (Kalamandeen and Gillson, 2007). Monitoring CV may also indicate changes in habitat quality which can be useful to assess the effects of different

* Corresponding author: Tel.: +36 1 3918626; fax: +36 1 3918655. E-mail addresses: [email protected] (F. Samu), [email protected] (P. Csontos), [email protected] (Cs. Szineta´r). 0006-3207/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2008.03.015

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management practices and the impact of other environmental factors (Niemela, 2000). Our aim was to describe CV associated with the local biotic component of the area investigated. In such an approach there are two questions that need to be dealt with: which taxa and which indicator parameters of the chosen taxa should be used. Both choices can seriously affect the final prioritization. On one hand, various limitations usually make it necessary that assessment is based on only few taxa (if not on one). If only few taxa can be monitored, then care has to be taken in the choice, since different organisms have different tolerances to environmental factors, they show their responses at different scales, although their responses may still correlate (Reid, 1998). On the other hand, the choice of parameters is usually less restricted. Making a choice that biases the total information content can affect the conclusion of the study. The often used diversity measurements, for instance, do not take into consideration any specific properties of the units (e.g. species) they enumerate. In this way, increased species diversity might suggest an increased CV for places where exotic species establish (Nebbia and Zalba, 2007). Diversity measures are also sensitive in small habitat patches to tourist species from neighbouring habitats (Hopkins and Webb, 1984). Other single parameter measures, like arthropod biomass index, might be insufficient as a measure of general CV, but can be very useful to describe specific qualities, such as the CV of a habitat for birds (Shochat et al., 2005). In the present study we chose one higher taxon, spiders (Araneae), and used several community related criteria to express the CV of the investigated habitat patches. Our conviction was that CV can be best expressed through a consensus of several, biologically meaningful criteria that represent many aspects of the indicative power of communities. We performed this multi-criteria analysis through ordination in order to get a combination of variables where the variance, and hence the sensitivity of the analysis, is maximised. Regarding this combination of variables as the multi-criteria measure of CV, we worked backwards to find which of the original variables – acting now as a surrogate measure – was the most strongly correlated with the multi-criteria measure. We validated the multi-criteria measure by examining its correlation with the results of a botanical survey. Finally we also tested for the robustness of the surrogate measure by calculating it for subsets of the sampling rounds and for a single spider family, and by correlating these restricted span results to the full sample multi-criteria measure.

2.

Materials and methods

2.1.

Study sites

We conducted two studies in two ecologically contrasting regions of Hungary. In the first region we had three lowland sites in the Kiskunsa´g National Park. The area of the Kiskunsa´g NP belongs to the Hungarian Great Plain, therefore we refer to these sites as the Great Plain (GP) sites. The last original vegetation of GP (prior to extensive land use for agricultural purposes) was forest-steppe. Forests were sporadi-

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cally interrupted by glades of different sizes supporting mainly steppe grasslands, but because of edaphic reasons wet meadows and alkaline meadows were also present to some extent. The sites lay in the vicinity of the settlements Fu¨lo¨psza´lla´s, Kunszentmiklo´s and Kunpesze´r; each site consisting of a mosaic of various habitat patches lying in the very narrow range of height of 87–95 m above sea level. Individual habitat patches could be assigned to various habitat types. The habitat types that were present at the sites represented different degree of present disturbance or disturbance history and could be identified as: arable land, pasture, alkaline salt marsh/alkaline meadow, wet meadow and loess steppe (Appendix A). In the second region, in the Buda Hills (BH), there was only one site which was located near Budapest, in the vicinity of the village Nagykova´csi. Here the original zonal vegetation was oak forest, but during past centuries this was cleared for pastures, arable land and orchards at many places. Recently most of these agricultural practices have been abandoned and at present large areas are in an old-field state. Here we also find a disturbance gradient among the nonwooded habitats, though the grassland component in the BH region is different. The habitat types were as follows: arable land, mown meadow, old-fields, rock steppes. In the BH region the terrain was more varied, individual habitat patches were distributed between the heights 306–408 m above sea level. In both regions the study units were habitat patches and the spider and plant communities that were present at these patches. A patch could be described as a continuous occurrence of a certain habitat type surrounded by habitat patches of other types, or delimited by bordering elements, such as roads, canals, etc. Patches were chosen so that they represent the above listed non-wooded typical habitat types for the respective regions. The degree of disturbance was mostly inherent to the habitat type, within type variations in disturbance were captured by the botanical assessment. Locality data, area and habitat identity of the 17 GP and 14 BH habitat patches that were sampled for spiders and assessed botanically are listed in Appendix A.

2.2.

Survey methods

Spider sampling was conducted in the GP habitat patches between 25.04.2001 and 10.06.2003, and in the BH region between 15.06.1999 and 14.11.2000. We used a hand-held motorized suction sampler to collect spiders (Samu and Sa´rospataki, 1995), which collects from an area of 0.01 m2. Ten such applications from a cumulative area of 0.1 m2 comprised one sample. In each habitat patch and on each sampling occasion 10 such samples were taken. The samples were taken in a transect of ca. 100 m in length. If patch diameter was smaller than 100 m, then transects were positioned in the approximate middle of patches. In larger patches transects were placed at 50 m from the edge. Coordinates in Appendix A. represent actual starting points of the transects. Habitat patches were revisited approximately monthly during the periods when it was possible to work on the field (e.g. no snow cover or flooding). For patch-level details of sampling effort see Appendix A.

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The botanical survey was carried out in the GP habitat patches between 7 and 12 August 2003; and for BH habitats between 1 September and 20 October 1999, with a rechecking of the evaluation between 26 and 30 June 2003, to make sure it was consistent with GP evaluations done in the same year. Two community variables: floristic diversity and disturbance status were estimated by expert judgements using ordinal scales. For the diversity estimation four states were distinguished (‘‘1’’ = the least diverse stage, where large monospecific patches of the dominant species characterised the habitat, mixed with few number of subordinated species; ‘‘4’’ = the high diversity end of the scale was given to habitats supporting high number of species representing a wide variety of life forms). For the disturbance status evaluation a seven-staged scale was applied (‘‘7’’ = the most disturbed state was given to habitats where the original natural vegetation was completely replaced by either crop plantations or dominating stands of invasive weeds; whereas ‘‘1’’ = the least disturbed state was used when the habitat had pristine vegetation with several rare and endemic species, without weeds or traces of grazing, trampling). Scores of the two botanical variables at the patches are listed in Appendix A.

2.3.

Multi-criteria assessment of conservation value

2.3.1.

Multi-criteria assessment of conservation value

We used seven spider community related variables to indicate CV of the habitat patches. These were either species character based variables, for which various species character values were obtained from an extensive background database: (1) frequency and (2) abundance based rarity, (3) association to natural habitats, (4) specialist status. These variables were calculated as the arithmetic mean of the character values of each species present in a given community. In the present study the inspected within community distributions of species character values were not very skewed (for the 124 distributions 67 (54%) were not significantly different from normal by Kolmogorov–Smirnov test at P = 0.05; mean skewness = 1.51), but in other studies where the characters might show severely skewed distributions, describing the central tendency by median or geometric mean could be considered. The other variables were community parameters, related to the species distribution of the community at the given patch: (5) species richness, (6) species evenness and (7) functional diversity. The resulting seven variables were not used to calculate any single compound value of CV, instead, the multivariate nature of CV was retained, and the gradient of the communities in this ‘‘multi-criteria space’’ was assessed by Detrended Correspondence Analysis, DCA. This method is commonly applied to arthropod community data (Basset et al., 2004; Bragagnolo et al., 2007). It stretches out the arch effect of traditional ordination methods, ensures that furthest points in a gradient are the most different considering all original variables, but will also work correctly with data that produces no arch effect. To carry out DCA we used PCORD version 4 (McCune and Mefford, 1999), which overcomes instability in plot ordering as shown by Oksanen and Minchin (1997). We used the scores of the habitat

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patches on the first axis of the DCA plot as the multi-criteria measure of CV.

2.3.2.

The background database

We obtained species character values from a broad background database, so that CV judgements in the case of species character based variables were based on a large sample size and a wide selection of habitat types, and not on local data alone. To build a background database, results from all previous projects, that involved sampling spider communities either in natural or agricultural areas had been stored in a project independent database. This database holds the actual sampling results, sampling meta-data, project meta-data, and site meta-data. For detailed description of the data model see Samu (2000). Records have been systematically stored in this database since 1993, and by now it holds data about 288 000 spider specimens. Most data have been collected in the various projects of the first author, while a considerable amount of data, mostly from natural habitats, belongs to the third author. In cooperative projects others also contributed their data to the database; they agreed in using their data, and are thanked in Acknowledgements. For the present analyses we used a subset of the background database limited to adult individuals (those were identified to species level) and to habitat patches where sampling was done on at least three different occasions, and at least 50 adult spider specimens were collected. This resulted in 158 habitat patches, 119, 890 adult individuals, belonging to 600 species.

2.3.3.

Measures of rarity

Describing rarity alone is a complex task, to which many approaches could be used (Gaston, 1994). For this reason we decided to use two different measures, both are called ‘‘global’’, because they are calculated from the global background database and they do not represent rarity in the concrete sampling areas. Global Frequency Value (variable 1) is derived from the species character GFVi, which is the proportion of localities where out of all sampled localities in the background database (N = 158 for the present case) species i occurs. This can be regarded as a measure of rarity that takes into account the spatial distribution of species. Widely occurring species are considered frequent and species occurring at few locations are considered rare, irrespective of their local abundance. Global Abundance Value (variable 2) is calculated from GAVi, which gives the proportion individuals of species i represents out of all individuals in the background database. This variable treats species as abundant if individuals are found altogether in high abundance. GAVi distinguishes between two rarity scenarios: species which occur at few locations, but are abundant there (as is the case with some specialist species) and between ‘‘true’’ rarities which occur only at few places and in small abundance.

2.3.4.

Measuring naturalness

To assess whether a species occurs rather in natural, pristine habitats or found in disturbed places, we first created nine naturalness categories by giving a score between 1 (= most

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disturbed) and 9 (= pristine) to the considered habitat patches in the background database. The mean of these scores for the background database was mean ±SD = 4.11 ± 2.168, while in the studied habitat patches it was mean ± SD = 4.05 ± 1.874, not significantly different from each other. To express the Naturalness Value (variable 3), for each species i we calculated its NVi from the proportion of individuals that occur in category k (relative to all individuals in that category), weighting this proportion with the category value and summing across all category values: X9 nik =n:k  k NVi ¼ k¼1 ni:

2.3.5.

Measuring specialist status

Specialist species which can only be found in a narrow spectrum of habitat types are of greater CV than generalist species, which can occur in a wide range of habitat types. To quantify the specialist value (SVi) of a species we used a special adaptation of the Indicator Value Analysis (IVA) by Dufrene and Legendre (1997). Since the Indicator Value (IVi) of species i depends on the habitat classification to which the analysis is applied, a species will have different IVi under a rough or a detailed habitat classification. If we make a hierarchical habitat classification and apply IVA at each level of the classification, then the more finer level of classification at which a species shows maximum IVi, the more specialist the species can be considered. To implement this procedure, we first coded habitats in the background database into 24 categories based on the Hungarian Habitat Classification System (Fekete et al., 1997), which served as the finest level (partition 5) of the classification. To create a next, rougher classification (partition 4), we merged these categories into 14 categories. In partition 3 we had four categories; partition 2 consisted of two categories (open and wooded); and partition 1 contained ‘‘all habitats’’ as one category. Details of habitat classes and partition creation are given in Appendix B. Apart from the default delimitation of the database we used for all analyses (see above), we only included species from which more than 50 individuals were caught in total in this analysis. This resulted in 195 species, for each of which five separate IVA (for the five partitions) were performed using the software IndVal v. 2.0 (Dufrene and Legendre, 1997). These analyses gave an IVi profile for each species, from which we had to extract a significant maximum – if there was one – and then had to record at which partition level the maximum occurred. Maximum at partition 5 (SVi = 5), would mean a specialist, while at partition 1 (SVi = 1), a complete generalist species. Significance of the maximum was judged by three criteria: (a) whether the given IVi value was the maximal out of the five; (b) how this value ranked among the 1000 values gained by the Monte Carlo simulation (Dufrene and Legendre, 1997), whether this rank was the best compared to ranks in the other partitions; and (c) whether the Monte Carlo simulation indicated the significance of the value (at P = 0.05). If all three criteria were met, we assigned the value of the partition as the Specialist Value of species i. Out of the species examined, 95 had a SVi assigned. In the BH region out of all species 39% had an SVi, in the GP region this ratio was 34%, both are

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much better than overall ratio in the background database, which was 16%.

2.3.6.

Community parameters

We chose to describe three community parameters: species richness, the evenness of the species distribution and a functional diversity measure. To describe the total number of species in the community we used ACE (Abundance-based Coverage Estimator; variable 5) (Chazdon et al., 1998), a nonparametric estimator which was computed by the EstimateS program (Colwell, 2005). For evenness we used Shannon evenness (E; variable 6), calculated as E = H/ln(S), where H is Shannon diversity and S is the number of species sampled (Magurran, 1988). To represent functional diversity, we counted the number of families in the samples of a given patch (variable 7). Spider families represent relatively homogenous feeding ecologies, and therefore they are good surrogates for functional groups (Uetz et al., 1999).

3.

Results

In the GP region 25,525 spider specimens were collected, out of which 6256 individuals were adults, representing 170 species from 17 families; in the BH region 17,517 individuals, 2451 adults, 115 species and 19 families were collected (for a complete list of species and their relative densities in the main habitat types refer to the Digital Annex). First we wanted to see whether we can treat and compare habitat patches from different sites together in the same analysis, or should we run separate analyses by regions or even by sites. To make these judgements, we ran an ANOVA analysis which considered habitat types as nested in site (unbalanced nested design: not all habitat types occurred at each site, but some occurred at more than one); sites nested in region; and also considered the area of the habitat patches as a covariate. Results for each of the parameters are shown in Table 1. Since habitats were deliberately chosen to represent different qualities, most parameters showed significant difference in that respect. The main effect of region was also highly significant for a number of parameters, indicating that the different setting and other likely regional differences (e.g.

Table 1 – The dependence of conservation value (CV) variables of local spider communities in a habitat patch on habitat type, site, region and area of habitat patch (covariate) Effect

d.f.

GAV

GFV

Habitat type (site, region) Site (region) Region Area

11

*

***

***

**

*

2 1 1

SV

NV

Family

E

ACE **

***

* **

Results of a nested ANOVA models with covariate. Significance of effects: *: P < 0.05, **: P < 0.001, ***: P < 0.0001. (CV variables: GAV = abundance based rarity; GFV = frequency based rarity; SV = specialist value; NV = naturalness value; Family = number of families; E = Shannon evenness; ACE = species richness; refer to text for further details).

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different species pool) had important effect. Sites (after controlling for habitat type), on the other hand, virtually did not differ within the GP region (in the BH region there was only one site). Habitat patch area had no effect on the studied

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parameters. This result led us to perform analyses separately for the GP and BH regions. The seven CV related variables provided a multi-criteria space, in which we used DCA ordination to reveal any

Fig. 1 – DCA ordinations of the Great Plain and Buda Hills region habitat patches in the multi-criteria space of seven variables (see text for details). For description of habitat patch IDs, see Appendix A. Cumulative correlation of distance matrix in reduced dimensionality space with distances in distance matrix with the original dimensionality (McCune and Grace, 2002): (1) Great Plain region: R2(axis 1) = 0.774, R2(axis 1 + 2) = 0.923. (2) Buda Hills region: R2(axis 1) = 0.888, R2(axis 1 + 2) = 0.976.

Table 2 – Correlation (r) and partial correlation (q) of original conservation value (CV) variables with the multi-criteria CV measure (DCA axis 1 scores) CV Variable GAV GFV SV NV Family E ACE

Buda Hills region (N = 14)

Great Plain region (N = 17)

r

P

q

P

r

P

q

P

0.97 0.93 0.70 0.79 0.71 0.08 0.89

0.0001 0.0001 0.0052 0.0007 0.0043 0.7768 0.0001

0.974 0.117 0.426 0.439 0.761 0.753 0.981

0.001 0.783 0.292 0.277 0.028 0.031 0.001

0.91 0.77 0.27 0.72 0.82 0.04 0.86

0.0001 0.0003 0.2974 0.0012 0.0001 0.8807 0.0001

0.784 0.439 0.228 0.352 0.038 0.774 0.776

0.0043 0.1772 0.5000 0.2884 0.9126 0.0052 0.0050

Degree of freedoms for calculating P were N2 for r, and N8 for q (see Table 1 legend for the description of CV variables).

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gradient of the studied spider communities. Communities were clearly ordered (Fig. 1), with the disturbed arable communities on one side, and the natural, least disturbed loess steppe, wet meadow (GP region) and rock grassland communities (BH region) on the opposite side of the gradient. We represented spider multi-criteria measure of CV as the axis score of communities on the first DCA axis. The multi-criteria measure had the strongest correlation with GAV in both studies, followed by the other rarity measure GFV, estimated species richness (ACE), naturality and family richness (Table 2). The partial correlations revealed that many of these variables

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had in fact low partial correlation values; i.e. when fixing the values of the other variables, those effects alone were not significantly correlated with the multi-criteria measure. On the other hand, GAV and ACE had both highly significant correlation and partial correlation with the multi-criteria measure in both regions (Table 2). To judge how spider multi-criteria measure relates to other possible CV measures, we compared it to a botanical CV assessment. This consisted of two variables: floristic diversity and disturbance. Similarly to the spider multi-criteria measure, we applied PCA to find the axis giving the

Fig. 2 – Relationship between spider (multi-criteria) and botanical (two criteria) CV measure for the GP and BH studies respectively. For description of habitat patch IDs, see Appendix A. Solid line: fitted linear regression line, dotted line: 95% confidence limit of fitting. GP region: R = 0.769, N = 17, P = 0.0003; BH region: R = 0.799, N = 14, P = 0.0006.

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Table 3 – The performance of Global Abundance Value (GAV) obtained from various restricted samples as compared to spider and botanical multi-criteria measures Region Buda Hills

Great Plain

Restricted sample year or Taxon

N

rspider

1999 2000 Linyphiidae 2001 2002 2003 Linyphiidae

14 14 14 17 17 17 17

0.93 0.83 0.58 0.63 0.63 0.55 0.73

P

rbotanical

P

<0.0001 <0.0001 0.03 0.007 0.006 0.02 0.001

0.81 0.83 0.39 0.64 0.67 0.58 0.54

<0.0001 <0.0001 NS 0.006 0.003 0.014 0.025

Correlation of i) GAV of yearly restricted samples (two sampling periods each year) and GAV of taxonomically restricted samples (only the most abundant family, Linyphiidae considered, but pooled for all years and samples) with DCA 1st axis values derived from the full spider dataset (rspider) and with the PCA 1st axis derived from the botanical assessment (rbotanical) in the Buda Hills and Great Plain regions.

maximal variance for this two dimensional case. We represented botanical multi-criteria measure as the axis score of sites on the first PCA axis. First PCA axes represented 87.75% and 97.43% of total variance in the GP and BH studies, respectively. In the two dimensional case PCA rotates the axes so that the original variables equally correlate with the axes (disturbance: rdisturb; floristic diversity: rdivers). These correlations with PCA axis 1 for the two regions were the following: BH: rdisturb = rdivers = 0.98; GP: rdisturb = rdivers= 0.91. Intercorrelation between variables were near complete in the BH region (r = 0.95) and still high, but not as high to make the use of both variables redundant, for the GP region (r = 0.65). The spider and botanical CV gradients showed very similar habitat patch ordering (Fig. 2). We experienced highly significant correlation between botanical and spider CV assessment in both studies (Fig. 2). Thus, the multi-criteria measure based on several variables of the spider communities coincided well with CV judgement based on assessing botanical values. But, would it be possible to obtain a simpler method to assess CV using spiders? To select a single variable that could be used as a surrogate for the multi-criteria measure, we have chosen the one which had the highest correlation (and also high partial correlation) with the multi-criteria measure. This variable occurred to be GAV in both studies (Table 2). To see how GAV alone could be used to assess CV and also to test for the robustness of this variable, we generated restricted sampling protocols which we applied to our original dataset. We created yearly restricted samples: for each year (BH: 1999, 2000; GP: 2001, 2002 and 2003) we took a subset of our dataset consisting of two sample rounds during the late spring – early summer period, which was the most species rich, a similar protocol that was applied by Scott et al. (2006). By pooling data from these two samples, for each year and each habitat patch we calculated GAV. We also investigated how GAV performed when it was calculated for a taxonomical subset of the data – for a single family only. These calculations were applied to the most abundant family, Linyphiidae. In each year we had a significant correlation between the yearly restricted sample GAVs and both the spider and the botanical multi-criteria measure obtained with the original full sample dataset (Table 3). In the case of linyphiid GAV we had significant correlation in the GP region with both spider and botanical

multi-criteria measure, and in the BH region with spider multi-criteria measure (Table 3).

4.

Discussion

Can spider communities be used to indicate conservation value? To answer this question we can depart from the postulation that spiders are an important and representative group of arthropods in most terrestrial ecosystems (Wise, 1993). To indicate CV, it is logical to select few representative components of the system which are: (a) ubiquitous in most target systems; (b) which has a fair number of species with a wide spectrum of responses; and (c) which are easy to sample in standardized ways. Since diversities of different taxa are often correlated (Reid, 1998), and spiders are in an intermediate position in many food webs (Wise et al., 1999; McNabb et al., 2001), we might expect that because of the extensive functional relationship to many other ecosystem components, spider indication of CV will be correlated with that of many other taxonomic groups. Here this prediction was tested by comparing spider CV to botanical CV. The present results supported our prediction and we could identify a very robust positive relationship between plant and spider based evaluation of CV. Similar results were reached in the analyses of various Swiss grasslands where spiders did not only sensitively indicate management differences (Pozzi et al., 1998), but spider indication was significantly correlated with the diversity of bugs and botanical parameters (Schwab et al., 2002). In Welsh peat bogs the richness of indicator spider species correlated very strongly with that of 13 invertebrate orders (Scott et al., 2006). While the correlation between spider community richness and composition and other parameters of the biota, especially vegetation structural richness, seems to be very general (Samu et al., 1999), other studies and modelling work indicate that the relationship to different taxa can be varied, since different organisms scale their environment differently, and therefore may have very different responses at various spatial and/or temporal scales (Weibull et al., 2003; Jepsen et al., 2005). This underlines that although it is a valid approach to find indicator taxa and easy-to-carry-out protocols, the more taxa of different responses are involved in an evaluation, the more founded

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results can be obtained (Oliver and Beattie, 1996; Bonte et al., 2006). If more than one taxa are considered concurrently then not only the resultant indicator power is increased, but we also gain the opportunity to observe and interpret the likely differences between the responses of these taxa. For the present study it would mean the scrutinization of cases where botanical and spider assessment disagrees to a greater extent. In the GP region wet meadows and loess steppes belong to the least disturbed, high diversity vegetation types where among the many protected species of the multi-layered herbaceous vegetation Iris sibirica and Schoenus nigricans can be mentioned for wet meadows, whereas Centaurea sadleriana and Orchis coriophora characterise the loess steppes. At the Kunpesze´r site botanical assessment could not differentiate between the CV of the representatives of these habitat types (‘‘kpla’’ vs. ‘‘kplo’’, Fig. 2a), whereas spiders clearly marked the wet meadow patch as of higher CV than the loess steppe patch. Spider species number and abundance was higher at the more moist, mesotrophic wet meadow (‘‘kpla’’) where several rare species were caught. Cnephalocotes obscurus (Blackwall, 1834) and Glyphesis taoplesius Wunderlich, 1969 are rare linyphiids which are typical of wet areas. In contrast to the wet meadow, at the loess steppe patch species that are characteristic for xerotherm habitats were found, such as the jumping spider (Salticidae) Synageles hilarulus (Koch, 1846), a typical loess steppe species either on lowland or hilly areas, and Euryopis quinqueguttata Thorell, 1875 (Theridiidae), another typical, but rare species of dry grasslands. While ‘‘kplo’’ and ‘‘kpla’’ were botanically of equal CV, another patch of the Hungarian Plain typical habitats, the alkaline grassland ‘‘fszszik’’ was evaluated, together with other similar habitat patches, only as of medium CV by the botanical assessment. These areas have rather low floristic diversity due to high abiotic stresses of the habitat (too wet in spring, too dry during summer and the salt concentration is high all over the year). However, its two-layered vegetation with Puccinellia limosa (upper layer) and Lepidium crassifolium (lower layer) provides a notable synphysiognomic structure, and carries intrinsic values. Spiders ranked some of these patches, such as ‘‘fszszik’’, rather high. Indeed this particular patch has a notable spider fauna. It is rich in species and there are rarities, such as the linyphiid Silometopus ambiguus (Cambridge, 1905) which is rare in whole Hungary, but here 1171 specimens were caught. The situation is similar with another linyphiid Metopobactrus deserticola Loksa, 1981, described not so long ago from Hungary and not yet found in other countries, typical but rare in alkaline meadows; here 81 specimens were caught. It is interesting to observe that at places where both latter species are abundant in their typical alkaline habitats, they spill over to neighbouring ‘‘ordinary’’ habitats (see Digital Annex) where under normal conditions they would not be found. From these species level examples it should be obvious that the disagreement between the outcome of the botanical and spider CV evaluation is due not to the taxon used, but to the fact that the latter takes into account species characters, such as specialist status and rarity, while the former does not.

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The very same picture emerges in the Buda Hills region, where the botanical assessment did not differentiate among the four xerotherm rock grassland patches (jmsz1-4, Fig. 2b), all dominated by Festuca valesiaca. All the four localities were undisturbed with high diversity vegetation coloured by several protected species like Dictamnus albus, Sternbergia colchiciflora, Pulsatilla grandis. On the other hand, the four patches had different spider multi-criteria measure values. The most valuable patch was ‘‘jmsz1’’, which is one of the two localities from where Pelecopsis loksai Szineta´r & Samu, 2003 (Linyphiidae) was recently described and where other rock grassland rarities, such as Trichoncus auritus (Koch, 1869) and Sintula spiniger (Balogh, 1935) (both Linyphiidae), were found. In contrast, ‘‘jmsz3’’ either did not have these species, or had fewer of them, and harboured other, generally more common species, such as Meioneta simplicitarsis (Simon, 1884) (Linyphiidae) or Xysticus kochi (Thorell, 1872) (Thomisidae), which are more representative of disturbed habitats. Looking up in our field books, the likely difference between these sites was the difference in vegetation height – ‘‘jmsz1’’ had higher and more structured vegetation. Vegetation complexity and the rich spider community that associates with it might as well hide CV differences. In the BH region, for instance, the above mentioned rock grassland stand ‘‘jmsz3’’ was equal in spider CV to ‘‘jmfka3’’, an old-field habitat patch characterised by low floristic diversity and by the abundance of the alien Stenactis annua – a compositae herb that indicated high disturbance here. To sum up, in our view, pointing out such discordances are the real value of a multi-taxa approach, which at the end may help to make more sound conservation decisions. While, as discussed above, complex multi-taxa approaches have their merits in evaluating CV, often there is need for rapid assessment, for measures that are easy to interpret. Financial limitations and lack of specialists might make it also necessary to apply simplified protocols. It is very notable that in both studies we had the same parameter, average abundance-based rarity, which was the most strongly correlated with the multivariate consensus of all parameters. Rarity status have been in other studies shown to be a good predictor of ecological integrity (Samu and Szineta´r, 2000). Rare species were shown to be reduced most due to the loss of heath and wood habitats (Cameron et al., 2004). Rare spiders were also the most sensitive to environmental changes, especially human disturbances (Decleer, 1990; Oxbrough et al., 2006). In another Hungarian study out of several parameters the abundance of rare spiders was the only parameter that could distinguish between relatively small grazing intensity differences } ts and (Pe´ter Bata´ry, Andra´s Ba´ldi, Ferenc Samu, Tama´s Szu } s, unpublished data). Similarly the richness Sarolta Erdo and abundance of rare harvestmen species indicated the best habitat quality in Brazilian forest fragments (Bragagnolo et al., 2007). Average rarity was not only a good predictor of CV in both regions of the present study, but it had a very good performance in various restricted protocols. Calculating average rarity from only two early summer samples in all cases showed significant correlation with the multi-criteria,

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several years CV assessment, either botanical or based on spiders. The same way GAV proved to be a robust measure, if the dataset was subsampled by the most abundant family. High partial correlation between GAV and the multi-criteria measure revealed that the observed high correlation of GAV is not an artefact arising from it being correlated to other influential variables; GAV performed very well when all other variables were controlled for (Bailey, 1981). In terms of partial correlation ACE was as good as GAV. Thus, in cases when background data on species rarity is not available ACE can replace GAV as a surrogate measure of conservation value. The good performance of ACE also strengthens the conclusion that rarity plays an important role in CV indication, because the calculation of ACE is done from rare species (abundance <10) in the community (Colwell, 2005). We can conclude that, based on the present studies, spiders proved to be very useful indicators of conservation value. If spider community parameters are considered along with species characteristics, a multi-criteria evaluation can be advised which produces habitat evaluations consistent with botanical assessments, but which evaluation also highlights special values reflected by the spider community. Should we need to choose only one parameter, then average

Site (region)

Fu¨lo¨psza´lla´s (GP)

Kunszentmiklo´s (GP)

Kunpesze´r (GP)

Nagykova´csi (BH)

Patch

fszfelh fszku fszlosz fszluck fszlucny fszszik fszsziny fszzsio kmbu kmsztr kmsztrsz kmurm kmvsz1 kmvsz2 kpla kplo kplu jmfka2 jmfka3 jmflu1 jmka1 jmka2 jmka3 jmka4 jmmg1 jmmg2 jmmg3 jmsz1 jmsz2 jmsz3 jmsz4

Coordinates

N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N

46°48.382 0 46°48.756 0 46°48.541 0 46°48.687 0 46°48.569 0 46°48.603 0 46°48.657 0 46°48.533 0 47°00.843 0 47°00.875 0 47°01.040 0 47°00.871 0 47°01.047 0 47°00.862 0 47°03.555 0 47°03.476 0 47°03.451 0 47°32.789 0 47°32.845 0 47°32.406 0 47°32.010 0 47°32.065 0 47°33.013 0 47°32.255 0 47°32.861 0 47°32.947 0 47°32.898 0 47°32.971 0 47°32.891 0 47°32.882 0 47°32.951 0

E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E

19°11.105 0 19°10.681 0 19°11.532 0 19°11.358 0 19°11.019 0 19°11.521 0 19°11.119 0 19°11.488 0 19°09.430 0 19°09.358 0 19°09.181 0 19°09.329 0 19°09.156 0 19°09.287 0 19°17.536 0 19°17.536 0 19°17.656 0 18°55.582 0 18°55.844 0 18°55.837 0 18°56.134 0 18°56.330 0 18°55.830 0 18°56.642 0 18°55.960 0 18°56.037 0 18°56.016 0 18°55.541 0 18°55.257 0 18°54.991 0 18°55.215 0

Area (m2) 27861 268988 7672 41111 95003 46488 83860 124188 19263 9729 116 25206 18122 28095 84139 9598 86311 55648 52724 141330 30770 100575 73606 9440 14432 12444 5940 10934 1888 3045 1944

1 4 1 ( 2 0 0 8 ) 1 3 1 0 –1 3 2 0

spider rarity is a very good surrogate for the multi-criteria evaluation, even if sampling is done with a restricted scope.

Acknowledgements The authors are grateful for technical assistance by Erika Botos, E´va Szita, Andra´s Szira´nyi, Zsuzsa Benedicty, Jo´zsef Ne´meth and Kiskunsa´gi National Park staff. We thank the permission of people who provided data for the Hungarian Spider Database: Ferenc To´th, E´va Szita, Kinga Fetyko´, Istva´n Ura´k. We thank Andrea Veres for statistical help. The projects were financed by the following grants: OTKA No. T048434, NKFP-6/013/2005. All three authors received Bolyai Scholarship during the studies. We thank the work and helpful suggestions by three anonymous reviewers.

Appendix A Locality information, sampling effort, habitat name, habitat class in ‘partition 5’ of the habitat classification system applied (see Appendix B), botanical and disturbance scores for habitat patches sampled in the Great Plain (GP) and Buda Hills (BH) regions.

No. of Total Habitat sampling spider class rounds catch (part 5) 20 18 22 22 20 22 21 20 17 19 18 17 17 18 19 19 16 8 9 7 9 9 9 8 9 9 9 10 10 7 8

646 753 1668 1177 1327 4691 3094 2934 701 1470 819 1085 1033 964 1698 1266 199 1720 1516 454 837 1147 1457 635 1091 897 886 1408 1670 2603 1196

13 18 12 17 17 6 6 2 17 12 13 13 7 7 4 12 18 13 13 13 14 14 14 14 17 17 18 11 11 11 11

Habitat name Pasture Arable Loess steppe Arable Arable Alkaline grassl. Alkaline grassl. Alkaline marsh Arable Loess steppe Pasture Pasture Alkaline grassl. Alkaline grassl. Wet meadow Loess steppe Arable Oldfield Oldfield Oldfield Mown meadow Mown meadow Mown meadow Mown meadow Arable Arable Arable Rock grassl. Rock grassl. Rock grassl. Rock grassl.

Floristic Disturbance diversity score score 2 1 3 1 1 2 1 1 1 3 3 1 2 2 4 4 1 3 2 2 2 2 2 1 1 1 1 4 4 4 4

4 7 2 7 6 3 3 3 7 3 3 3 2 2 2 2 7 4 4 4 5 5 5 5 6 6 7 2 2 2 2

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1319

1 4 1 ( 2 0 0 8 ) 1 3 1 0 –1 3 2 0

Appendix B Habitat classification in the spider background database, and their inclusion in partitions 1-5 for the calculation of Specialist Value (see text for details). Total catch of adult spiders in the background database is also included to indicate the weight of habitat classes in the analysis. Habitat type Reed beds and large sedge communities Pannonic (viper’s grass) salt marshes Bogs and mires Fens, rich fens and swamps Artemisia saline steppes Pannonic saline meadows Pannonic alkali hollow communities Inland sand dunes, Pannonic dune open grasslands Rock debris swards Semi-dry calcareous grasslands of colline and mountain regions Steppic grasslands of colline regions Steppic grasslands of lowlands Degraded grasslands of lowlands, old fields Degraded grasslands of colline and mountain regions Improved grasslands, intensive pastures Fallow lands and weedy vegetation of field margins Perennial crop fields Annual crop fields Willow galleries, mixed oak-elm-ash forests of rivers and swamp forests Lowland sessile oak forests (with sessile oak – hornbeam mixed stands) Beech forests and other hardwood forest of mountain regions Mountain and colline mesophylous oak forests Pannonian karst white oak low woods, colline xerotherm oak forests Pine plantations

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