The relative performance of taxonomic vs. environmental indicators for local biodiversity assessment: A comparative study

Ecological Indicators 15 (2012) 171–180

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The relative performance of taxonomic vs. environmental indicators for local biodiversity assessment: A comparative study Yael Mandelik ∗ , Tamar Dayan, Vladimir Chikatunov, Vasiliy Kravchenko Dept. of Zoology, Tel-Aviv University, Tel-Aviv 69978, Israel

a r t i c l e

i n f o

Article history: Received 4 July 2011 Received in revised form 21 September 2011 Accepted 24 September 2011 Keywords: Biodiversity survey Environmental indicator Indicator taxa Local scale Mediterranean ecosystem Surrogate taxa

a b s t r a c t Direct diversity measurements usually require more resources and knowledge than those available, so surrogates are needed. The two main types of surrogates are environmental indicators, physical characteristics of the environment, and taxonomic indicators, a taxon or subsets of taxa used to reflect other taxa in the ecosystem. The relative merit of these two surrogate types, though hotly debated, has rarely been investigated directly. Here we compared the relative efficacy of environmental vs. taxonomic indicators in representing local scale patterns of species richness, rarity, endemism, and composition. The study was conducted in the Mediterranean ecosystem of the Jerusalem Mountains and the Judean Foothills, Israel. A detailed study of eight taxa (vascular plants, ground dwelling beetles, moths, spiders, scorpions, diplopods, small mammals, and small reptiles) and of coarse-resolution habitat types and fine-resolution environmental indicators was conducted in 40, 1000 m2 plots representing the different habitats in the region. Fine-resolution environmental indicators generally outperformed taxonomic indicators in reflecting diversity patterns, but their performance varied considerably between taxa and diversity measures, and on average they conveyed less than 55% of the variation in diversity patterns. Coarse-resolution habitat types failed to reflect diversity patterns. Plant species richness contributed to representation of diversity patterns of some taxa, but it needs to be combined with structural aspects of the vegetation in order to improve prediction power and expand the diversity aspects addressed. Composition patterns were poorly represented by either indicator used. We conclude that fine-resolution environmental indicators are suitable for general indication of local richness, rarity, and endemism patterns in Mediterranean ecosystems. Mapping these patterns at high resolution requires direct detailed surveys, or the application of other indicators. © 2011 Elsevier Ltd. All rights reserved.

1. Introduction In the past decade significant progress was made in developing sophisticated, multi-faceted planning tools for biodiversity conservation (Sarkar et al., 2006). Often, however, these scientific achievements cannot be realized because the necessary ecological data, mainly spatial distribution of species diversity, is limited or missing and there are not enough time and funds for acquiring it (Prendergast et al., 1999). This can greatly reduce the effectiveness of conservation efforts as biased and incomplete data can lead to erroneous conservation priority setting (Grand et al., 2007). Direct species diversity measurements are usually too time, money, and knowledge demanding to perform, so surrogates are required (Noss, 1999). The two main surrogate categories used are

∗ Corresponding author. Permanent address: Dept. of Entomology, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel. Tel.: +972 8 9489224; fax: +972 8 9466768. E-mail address: [email protected] (Y. Mandelik). 1470-160X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.09.033

environmental indicators, physical characteristics of the environment expected to affect species distribution, and taxonomic indicators, a taxon or subsets of taxa expected to reflect other taxa in the ecosystem (Rodrigues and Brooks, 2007). Despite a wealth of research on different biodiversity indicators, a comparative approach in assessing their efficiency was rarely taken. The relative merit of environmental and taxonomic surrogates is hotly debated (Araújo et al., 2001, 2003; Brooks et al., 2004; Cowling et al., 2004; Faith, 2003; Faith and Walker, 1996; Hortal et al., 2009; Pressey, 2004). However, only few researchers have tested the two approaches directly focusing on the same case study (Bonn and Gaston, 2005; Carmel and Stoller-Cavari, 2006; Ferrier and Watson, 1997; Kirkpatrick and Brown, 1994; Powney et al., 2010; Reyers et al., 2002), and those who attempted to do so did not find a significant or consistent superiority in performance of either indicator type (but see Brin et al., 2009; Thomson et al., 2007). A recent review of this topic showed that taxonomic indicators generally out-perform environmental indicators for complementary reserve selection, but studies reviewed did not directly compare the two approaches (Rodrigues and Brooks, 2007).

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Two types of environmental indicators, differing in their resolution and ease of application, are most commonly used for species diversity assessments. First, coarse-resolution habitat and vegetation types are commonly used as quick and easy way of classifying main variation sources in biodiversity patterns during field surveys. These classifications are based on field surveys and remote-sensing data, focusing on conspicuous structural components of the area (e.g. gross vegetation forms such as open fields, shrubland and chaparral). The second type of environmental indicators is fineresolution intra-habitat characteristics including topography, soil, micro-climate, vegetation, and productivity (e.g. Anderson et al., 2007; Faith, 2003; Faith and Walker, 1996; Fleishman et al., 2003; Vaughan and Ormerod, 2003). Seldom are several data types compiled, and their application requires some processing and analysis to produce classification of the landscape. Some studies show low congruence of environmental indicators with actual diversity patterns (e.g. Araújo et al., 2001; Hortal et al., 2009) and a lack of congruence between results produced by different environmental variables and analytical procedures (Brooks et al., 2004; Mac Nally et al., 2002; Snelder et al., 2007; Trakhtenbrot and Kadmon, 2005). However, an overall perspective on the efficiency of different environmental indicators, especially the relative performance of coarse-resolution habitat types vs. fine-resolution indicators for diversity assessments, is still largely missing. Vegetation (both diversity and structural aspects) is the most commonly used indicator in conservation planning and management (Ferris and Humphrey, 1999), but studies of its indicative performance produce variable results (e.g. Lewandowski et al., 2010; Provencher et al., 2003; Vessby et al., 2002; Virolainen et al., 2000). Vegetation can affect faunal diversity patterns through its trophic role in the ecosystem, i.e. indicator taxa approach, and/or through its role in shaping the physical structure of the habitat, i.e. environmental indicator approach (Lawton, 1983). At a wide scale high floristic and structural diversity are expected to support higher faunal diversity (e.g., Dufour et al., 2006; Siemann, 1998; Williams et al., 2002; but see Tews et al., 2004). However, the relative efficiency of floristic vs. structural characteristics of the vegetation as diversity indicators is little known. Many land-use conflicts, especially in densely populated regions, occur at small spatial scales of a few square kilometers (Ferrier, 2002). Broad-scale diversity indicators may not be reliable at smaller scales, in which variation in diversity patterns is much lower and its detection requires high sensitivity (Kerr et al., 2000; Rahbek, 2005). While indicators used for regional to global diversity assessment should reflect mainly landscape context effects, localscale indicators need to be sensitive mainly to variations in habitat type and condition (Noss, 1990). The performance of both environmental and taxonomic indicators is scale-dependent and their relative efficacy may vary accordingly (Gaspar et al., 2010; Grenyer et al., 2006; Hess et al., 2006; Lewandowski et al., 2010). Here we directly compare the performance of environmental vs. taxonomic indicators in representing species diversity patterns at the local scale. Using multiple taxa and a broad spectrum of coarse- and fine-resolution environmental parameters and exploring their efficiency in representing patterns of species richness, rarity, endemism, and composition, allowed us to comprehensively evaluate the relative merits and shortcomings of each approach and its relative performance for conservation planning. Specifically we addressed the following questions:

(1) What is the relative efficacy of environmental vs. taxonomic indicators in representing local patterns of species diversity? (2) How reliable are coarse-resolution habitat types vs. fineresolution environmental indicators in representing local patterns of species diversity?

(3) Is vegetation a reliable indicator for species diversity at the local scale? What is the relative contribution of structural vs. floristic characteristics of vegetation in reflecting patterns of diversity? 2. Methods 2.1. Study system The research was conducted in the Jerusalem Mountains and the Judean Foothills, approximately 30 km southwest of Jerusalem, a region characterized by Mediterranean-type vegetation and high faunal and floral diversity and endemism (Yom-Tov and Tchernov, 1988). The area, located in the central, most densely populated region of Israel, is among the last remnants of a unique transient ecosystem, at the interface of the humid Mediterranean ecosystem to its north and the arid ecosystem to its south (Weizel et al., 1978). It is undergoing rapid and intensive development and a great deal of effort is invested in sustainably planning and managing it (Kaplan et al., 2000). We selected 5 landscapes with the largest open area (non-built or cultivated) in the region that collectively encompass the 6 major vegetation communities in the region (those that cover >5% of the overall area; dominated by Rhamnus lycioides L., Pistacia lentiscus L., Pistacia palaestina Boiss., Phillyrea latifolia L., Qquercus calliprinos Webb; Kaplan et al., 2000). We calculated the proportion of the following four main successional habitat types found in these landscapes: dwarf shrubland (batha; ca. 20% of the overall area), Mediterranean shrubland (garigue; ca. 20% of the overall area), open chaparral (open maquis; ca. 35% of the overall area), and dense chaparral (dense maquis; ca. 25% of the overall area). Spatial analysis was based on high resolution aerial photos (1:5000–7500) refined by ground-truthing. We established 40, 1000-m2 plots, located according to the distribution of the four habitat types in the region: batha (7 plots), garigue (8 plots), open chaparral (14 plots) and dense chaparral (11). Before deciding on the specific location of the study plots, we visually surveyed a larger area to make sure our sampling point was representative of the surrounding. Major disturbances in the region are grazing, fire, agriculture, settlements, and roads. All plots were at least 500 m from recently burnt area (<4 years), cultivated area, or settlements. Since grazing occurs throughout the region and since the road network in the area is dense, we could not avoid these disturbances, and therefore incorporated them in our sampling design. All our plots were under low intensity of cattle grazing and located at varying distances from roads (20–600 m; we refer to this factor in the analyses). 2.2. Coarse habitat classification and fine-resolution environmental indicators sampling We classified the four major successional stages found in the study area (dwarf shrubland, Mediterranean shrubland, open and dense chaparral) as our coarse-resolution habitat types. Fineresolution indicators were detailed measures of habitat structure and condition estimated in four, 10 m × 10 m quadrates per plot haphazardly located within the plot (if strong patchiness was evident, quadrates were located accordingly). In each quadrate we recorded ground cover of main plant species (>1%) and substrate types (bare ground, stones and rocks, litter). Vertical vegetation profile (layering) was determined by visually estimating the relative cover of each vegetation layer (annuals, semi-shrubs < 0.5 m, shrubs 0.8–3 m, trees > 3 m, and vines; following Feinbrun-Dothan and Danin, 1998) and the dominant species comprising each. In addition, we evaluated the complexity of the ground habitat and the vertical profile of the vegetation using the Simpson index on the different cover types and their relative abundances (Naveh and

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Whittaker, 1979). Altitude, slope, and aspect (in degrees) were also recorded for each plot. 2.3. Faunal and floral sampling We sampled eight taxa: vascular plants, ground-dwelling beetles, moths, spiders (mostly ground-dwelling), scorpions, diplopods, small ground-dwelling mammals, and small grounddwelling reptiles (mostly lizards). Sampling effort required for the faunal and floral surveys was established in a preliminary study using species accumulation curves (see Mandelik et al., 2002). Vascular plants were recorded in each plot along four 50-m transects, 5 m apart. Additional time was spent walking haphazardly in the plot recording new species encountered until no new species were found for 5 consecutive minutes (species accumulation curves level off after 5 min; Mandelik, 2005). Vascular plants were sampled twice, once in early and once in late spring (March and May respectively). The active ground-dwelling fauna (beetles, spiders, scorpions, diplopods, and some of the small vertebrates) was sampled with pitfall traps, 10 cm in diameter and 10 cm in depth, filled with approximately 100 mL of ethylene glycol to prevent predation and decomposition of specimens. In each plot 12 pitfalls were buried flush with the ground surface in a 10 m × 17 m grid. To decrease biases in pitfall catches due to microhabitat structure, we matched (to the extent possible) ground cover in a 2-m-diameter area around each trap and made sure no prominent obstacles were present (e.g., large rocks, trunks). We conducted five sampling sessions of one week each in the summer (August), autumn (November), winter (January), and early (March) and late (May) spring. Moths were sampled with 6 V ultraviolet light traps with approximately 20-m attraction radii (average across species; V. Kravchenko, unpublished data). A trap was placed at the center of each plot, in a relatively open patch, to standardize trap efficiency. Moths were sampled only in 25 plots, excluding 15 plots that were ≤100 m from the closest road, to avoid potential biases in species distribution assessments due to light pollution. Moths were sampled in early and late autumn (late September and November respectively), winter (January), early and late spring (March and May respectively), and summer (August). Samplings were conducted for one night each time, from dusk to dawn, during the first quarter of the moon cycle to standardize and increase trapping efficiency. Rodents were sampled with 12 Sherman live traps per plot in the same grid used for the pitfalls. We conducted three sampling sessions during summer (August) and autumn (October–November), corresponding to peak population sizes. Samplings were conducted for one night each, from evening to the next morning, during the first quarter of the moon cycle so as to standardize and increase trapping efficiency. Individuals were identified to species and released. As there is no reason to assume differences in re-capture rates between plots, we used trapping data to calculate relative abundances and diversity measures. Lizards were sampled in pitfalls and with 1 m × 1 m wood slabs placed at the center of each plot on a patch of bare ground. The slabs were placed in the plots before the first pitfall sampling session and left there throughout the sampling period (ca. one year). During each pitfall and rodent sampling session we recorded the fauna present underneath the slabs. All trapped specimens were deposited at the Tel Aviv University Zoological Museum, the National Collections of Natural History. 2.4. Preliminary analyses Some pitfall traps were damaged by animal trampling, fallen branches, etc. We therefore used a log-linear model to check the

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equivalency in actual sampling effort (i.e., number of undamaged traps [response variable] in each plot in each sampling round [the explanatory variables]). Significant differences in the number of undamaged pitfall traps were found between sampling periods but not between plots (plots, 2 = 50.00, df = 39, p = 0.12; sampling periods, 2 = 19.96, df = 4, p < 0.001). Similar results were found when comparing the number of undamaged traps in each habitat type in each sampling round (habitat type, 2 = 4.04, df = 3, p = 0.26; sampling periods, 2 = 20.16, df = 4, p < 0.001). Thus actual sampling effort was similar across sampling plots and habitats and we could compare them directly. Rarity analyses were carried out for the species-rich taxa (plants, beetles, moths, spiders) at two scales – a national/regional scale and a local scale. The former was based upon existing (external) databases, with experts consulted in order to address potential biases and gaps in spatial representativeness of these collections. Plants found in fewer than 100 sites in Israel were classified as rare, following Fragman et al. (1999); beetle and moth species with fewer than 15 specimens in the national entomological collections at Tel Aviv University (the only major collection of Israeli insects, nationally and worldwide) were classified as rare based upon the curators’ expert opinion (V. Chikatunov and V. Kravchenko respectively). Adjustments were made for a few species with large sampling biases, based upon the curators’ judgment. Due to limited data, national/regional rarity was not calculated for spiders. Local (relative) rarity scale was based on the relative abundance of each taxon in the present study: rare species were classified as those in the first quartile of the distribution (Gaston, 1994). Results were very similar between the two rarity scales in both model coefficients and model factors. We therefore present only the local scale as it is available for all taxa. Endemism (number of endemic species) to the Levant (including Lebanon, Syria, Jordan and Israel) region was determined for plants according to Fragman et al. (1999); for beetles and moths determination of endemism to the Levant region was based upon data from the national entomological collections at Tel Aviv University and curators’ knowledge. Due to limited data, endemism could not be established for spiders. We tested for spatial autocorrelation between plots using a Mantel test (Mantel; PC-ORD version 5, MjM Software, Gleneden Beach, Oregon) for the correlation between geographic distance (calculated with ArcGIS 9.2, Environmental Systems Research Institute, Redlands, California) and similarity between species assemblages (species similarity matrix using the Sorensen quantitative similarity index; Magurran, 2004) for each taxon. Spatial autocorrelations in species composition accounted for 0.5–16% of the variation and are thus of limited ecological significance (plants, r = −0.33, p < 0.001; beetles, r = −0.30, p < 0.001; moths, r = −0.40, p < 0.001; spiders, r = −0.36, p < 0.001; scorpions, r = −0.22, p < 0.001; diplopods, r = −0.26, p < 0.001; mammals, r = −0.40, p < 0.001; reptiles, r = −0.02, p < 0.001). We therefore did not further include geographic distances between plots in our analyses. 2.5. Statistical analyses 2.5.1. Environmental indicators The relationship between coarse-resolution habitat types and fine-resolution environmental indicators (i.e. intra-habitat characteristics) to species richness, rarity, and endemism were tested together using a model selection procedure based on AICc values (corrected for small samples; using SAM v4; Rangel et al., 2010). We additionally incorporated to the tested variables the distance of each sampling plot from the closest road, to test for potential effects of this factor on richness, rarity and endemism patterns. We used Redundancy analysis (RDA; Canoco for

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Table 1 Selected multiple regression models (based on AICc) for the effects of coarse-resolution habitat types and fine-resolution environmental indicators on species richness. Only variables with p < 0.05 were allowed in the final model. Taxon

Model r2

Model variables

B

SE

p

Beetles

0.67***

Moths

0.75***

Spiders

0.47***

Scorpions

0.45***

Diplopods

0.17*

Mammals

0.4**

Reptiles

0.285**

Plant species richness Slope Altitude Annual cover Foliage cover Vertical profile complexityb Plant species richness Altitude Aspect Plant species richness Rock and stone cover Ground habitat complexitya Vertical profile complexityb Plant species richness Habitat type Rock and stone cover Altitude Rock and stone cover Plant species richness Altitude Vegetation ground cover Annual cover Rock and stone cover Slope Aspect Vegetation ground cover

0.435 −0.331 0.141 0.039 0.016 −2.03 −0.256 0.095 0.04 0.284 −0.017 2.839 −1.32 0.224 −0.177 −0.017 0.03 −0.008 0.128 0.049 0.02 −0.02 −0.015 0.284 −0.03 0.018

0.144 0.163 0.044 0.008 0.008 0.604 0.127 0.022 0.014 0.07 0.006 0.869 0.628 0.089 0.071 0.009 0.012 0.003 0.059 0.019 0.008 0.005 0.007 0.096 0.014 0.006

0.005 0.05 0.003 <0.001 0.05 0.003 0.05 <0.001 0.009 <0.001 0.004 0.002 0.043 0.016 0.018 0.05 0.017 0.029 0.037 0.016 0.014 <0.001 0.027 0.005 0.034 0.009

a b * ** ***

Calculated using the Simpson index of diversity on the different types of ground cover and their relative abundances. Calculated using the Simpson index of diversity on the different vegetation layers and their relative coverage. Model p < 0.05. Model p < 0.01. Model p < 0.001

Windows 4.5, Microcomputer Power, Ithaca, NY) to explore the effects of coarse-resolution habitat types on species composition. Significance of ordination axes was tested by Monte Carlo permutation (unrestricted; 4999 permutations; Leps and Smilauer, 2003). To test the effect of fine-resolution environmental indicators on species composition we conducted a Principle Component Analysis (PCA; Canoco for Windows 4.5, Microcomputer Power, Ithaca, NY) on the fine-resolution environmental variables to extract main environmental variation axes while accounting for multiple variables inherently interconnected e.g. % cover of different substrate types. We then converted the PCA factors into a Euclidean distances matrix (a “habitat matrix”), representing the difference in environmental characteristics between plots. We used a Mantel test (Mantel; PC-ORD version 5, MjM Software, Gleneden Beach, Oregon) to explore the correlation between species similarity matrices, using the Sorensen quantitative index for all taxa, and the habitat similarity matrix. 2.5.2. Taxonomic indicators Cross-taxon correlations in species richness, rarity and endemism were tested using a model selection procedure based on AICc values (corrected for small samples; using SAM v4; Rangel et al., 2010). The distance of each sampling plot from the closest road was additionally incorporated to the tested variables to explore possible effects of this factor on patterns of richness, rarity and endemism. Cross-taxon congruence in species composition (overlap between taxa in compositional patterns) was tested using Mantel tests (Mantel; PC-ORD version 5, MjM Software, Gleneden Beach, Oregon) on species similarity matrices. For consistency, we used the Sorensen qualitative index for all taxa. To test for potential effects of plots’ proximity to roads on the performance of the different indicators, we incorporated this factor to the richness, rarity and endemism models of both the environmental and taxonomic indicators. To test for potential effects of

road proximity on species composition we repeated the composition analyses described above excluding 15 plots that were <100 m from a road (see Appendix A). The remaining 25 plots were at a minimal distance of 350 m from a road, beyond the maximal effects distance for most road edge effects (Forman and Alexander, 1998). While significance levels decreased for some models, all patterns (significant vs. non-significant results and coefficients) remained as in the original analyses (Appendix A). Throughout the analyses we did not apply a correction for multiple tests as our interpretation and discussion focuses on predictive values of indicators, reflected by correlation and regression coefficients rather than mere statistical significance (Moran, 2003). 3. Results We sampled 420 plant species, 424 beetle species, 111 moth species, 102 spider species, 5 scorpion species, 6 diplopod species, 8 mammal species, and 6 reptile species. The number of species and their abundances varied by more than 45% between plots for all taxa, allowing meaningful analyses of diversity patterns. 3.1. Environmental indicators Coarse-resolution habitat classification was not included in the final models selected for representing species richness, rarity, and endemism of neither taxon, except the scorpions’ species richness model and the moths’ endemism model (Tables 1 and 2). Similarly, coarse-resolution habitat types failed to distinguish differences in species composition, except for spiders (spiders: F = 2.64, p = 0.04). We found high variability between taxa in the prediction power of the fine-resolution environmental models for species richness, rarity and endemism; models accounted for 17–75% of the variation in species richness (46% on average; Table 1) and 40–73% of the variation in species rarity and endemism (53% on average;

Y. Mandelik et al. / Ecological Indicators 15 (2012) 171–180

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Table 2 Selected multiple regression models (based on AICc) for the effects of coarse-resolution habitat types and fine-resolution environmental indicators on species rarity (at a local scale) and endemism. Only variables with p < 0.05 were allowed in the final model. Taxon

Model r2

Model variables

B

SE

p

Rare species Beetles

0.53***

Moths

0.73***

Spiders

0.4***

Altitude Annual cover Ground habitat complexitya Vertical profile complexityb Altitude Rock and stone cover Plant species richness Vertical profile complexityb Aspect

1.167 0. 314 13.51 −12.734 0.706 −0.099 1.545 7.005 −0.13

0.312 0.051 5.2 3.328 0.134 0.045 0.405 2.007 0.065

0.001 <0.001 0.017 0.001 <0.001 0.038 0.001 0.001 0.05

Endemic species Beetles

0.52***

Moths

0.48**

Ground habitat complexitya Vertical profile complexityb Semi-shrubs cover Annual cover Trees and shrubs cover Habitat type Aspect Litter ground cover

−22.184 9.983 −0.158 0.146 −0.097 −0.487 0.063 0.037

6.34 4.25 0.046 0.048 0.038 0.129 0.022 0.018

0.001 0.025 0.002 0.021 0.015 0.001 0.009 0.05

a b ** ***

Calculated using the Simpson index of diversity on the different types of ground cover and their relative abundances. Calculated using the Simpson index of diversity on the different vegetation layers and their relative coverage. Model p < 0.01. Model p < 0.001.

Table 2). Species richness models of diplopods and reptiles, two of the species-poor taxa, had lowest prediction power, accounting for <30% of the variation. Plant species richness and the structure of the ground habitat were prominent features in the models of species richness (Table 1). The most important components of the ground habitat structure were vegetation ground cover, which had mostly a positive effect, and rock and stone cover, which had mostly a weak negative effect. The complexity measures (of the ground habitat and of the vegetation layering) were incorporated only in the moths’ and scorpions’ species richness models (Table 1). Among the geo-morphological parameters, altitude had a strong positive effect on species richness of beetles, moths, diplopods and mammals, while the aspect and slope varied in their effects (Table 1). In most of the rarity and endemism models the structure of the ground habitat was a prominent feature, similar to the richness models (Table 2). However, plant species richness was incorporated only in the rare spider species model, unlike its prominent role in the richness models. The complexity measures strongly affected patterns of rare moth and endemic beetle species (Table 2). Altitude and aspect were incorporated in some of the rarity and endemism models and their effects varied. Distance to the closest road was not incorporated in any of the richness, rarity and endemism models. In the PCA we conducted to extract main variation axes in habitat structure using the fine-resolution environmental variables, the first four PCA factors were selected, encompassing 79% of environmental variability between plots (% of total variation explained by factor 1 – 41.34, factor 2 – 16, factor 3 – 11.36, factor 4 – 10.5). Factor 1 is primarily linked to structure and complexity of the ground habitat (% ground cover of annuals, rocks and bare ground, and the Simpson diversity index for the ground habitat). Factor 2 is primarily linked to the vertical profile of the vegetation (% cover of trees and shrubs), and factors 3 and 4 are primarily linked to aspect (and to some extent to altitude) and plant species richness respectively. A subsequent PCA on the same environmental variables, excluding plant species richness was conducted to separate between structural and floristic effects of the vegetation. Very similar results were obtained for factors 1–3 (in terms of factor loadings and eigenvalues), while factor 4 was somewhat related to slope (factor loading = −0.6).

In the Mantel test correlating species similarity matrices and habitat similarity matrix (based on PCA factors; see Section 2) we found that the fine-resolution environmental characteristics accounted for 23–30% of the variation in species composition of the species-rich taxa while correlation coefficients were very low for the species-poor taxa (Table 3). Results obtained using the PCA factors where plant species richness was excluded produced highly similar results in terms of significance levels and coefficients.

3.2. Taxonomic indicators Multiple regression models for species richness accounted for 16–49% of the variation in different taxa, and failed to represent richness patterns of diplopods and mammals, two of the speciespoor taxa (Table 4). The environmental models for species richness had 0.08–0.33 higher r2 values compared to the taxonomic models, for all taxa except spiders. Plant species richness was incorporated only to the beetle and spider models, while spider richness was incorporated in all models but the reptile (Table 4). Similar superiority of the environmental models emerged when comparing rarity and endemism models (Table 5); the environmental models had 0.05–0.52 higher r2 values compared to the corresponding taxonomic model for all taxa. Taxonomic models accounted for 17–48% of the variation in rarity patterns, and failed to reflect patterns of endemism (Table 5). Plant rarity and endemism was not incorporated in any of the tested models. Distance to the closest road

Table 3 Results of Mantel tests for the correlation between species composition (using the Sorensen quantitative similarity index) and habitat structure (using Euclidean distances based on PCA factors). Taxon

r2

Z

p

Beetles Moths Spiders Scorpions Diplopods Mammals Reptiles All taxa combined

0.28 0.23 0.30 0.01 0.01 0.11 0.003 0.31

965.95 525.16 1457 545.4 1001.6 965.2 675.92 1499

<0.001 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

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Table 4 Selected multiple regression models (based on AICc) for the effect of cross-taxon congruence on species richness. Taxon

Model r2

r2 a

Model variables

B

SE

p

Diplopod species richness Spider species richness Plant species richness Mammal species richness Spider species richness Reptile species richness Scorpion species richness Plant species richness Beetle species richness Spider species richness Reptile species richness

1.202 0.804 0.388 0.719 −0.661 −0.442 0.328 0.153 0.112 0.544 −0.247

0.748 0.379 0.219 0.254 0.269 0.191 0.121 0.086 0.06 0.154 0.143

0.12 0.04 0.085 0.01 0.023 0.031 0.01 0.085 0.068 0.001 0.091

Scorpion species richness Spider species richness Beetle species richness

−0.392 0.737 0.234

0.144 0.258 0.11

0.01 0.007 0.04

Beetles

0.38

−0.29

Moths

0.42**

−0.33

Spiders

0.49***

0.02

Scorpions

0.37***

−0.08

Diplopods Mammals Reptiles Plants

ns ns 0.16** 0.41***

−0.17b −0.4b −0.125 –

***

ns – non-significant. a Differences between current r2 and the r2 of the corresponding environmental model for the same taxon (see Table 1). b Since the taxonomic model is insignificant, this value was set as the environmental model r2 . ** Model p < 0.01. *** Model p < 0.001. Table 5 Selected multiple regression models (based on AICc) for the effect of cross-taxon congruence on species rarity (at the local scale) and endemism. r2 a

Taxon

Model r2

Rare species Beetles Moths

0.48*** 0.385**

−0.05 −0.345

0.17** 0.45***

−0.23 –

ns ns ns

−0.52b −0.48b –

Spiders Plants Endemic species Beetles Moths Plants

Model variables

B

SE

p

Rare spiders Rare spiders Rare beetles Rare beetles Rare beetles

0.642 −0.481 0.255 0.127 0.754

0.405 0.262 0.078 0.026 0.136

0.12 0.08 0.003 0.009 <0.001

ns – non-significant. a Differences between current r2 and the r2 of the corresponding environmental model for the same taxon (see Table 2). b Since the taxonomic model is insignificant, this value was set as the environmental model r2 . ** Model p < 0.01. *** Model p < 0.001.

was not incorporated in any of the richness, rarity and endemism models. Statistically significant cross-taxon correlations in species composition were found between all taxa; however their strength and ecological significance varied considerably (Table 6). The ecologically meaningful correlations in species composition (accounting for 23–26% of the variation in species composition) were found between plants, beetles, and moths. Other cross-taxon correlations account for 0–14% of the variation in species composition. Plants represented 41% of overall composition variation (of all taxa combined, excluding plants), while the faunistic taxa had low representation rate (1–14% representation) (Table 6). Results obtained using the PCA factors where plant species richness was excluded produced highly similar results in terms of significance levels and coefficients. 4. Discussion Our results do not support using coarse-resolution habitat classification for diversity assessments in the Mediterranean ecosystem studied. It is therefore necessary to conduct detailed field surveys and collect fine-resolution data for conservation planning and management of this ecosystem. Three main patterns concerning the use of fine-resolution environmental indicators vs. taxonomic indicators emerged from our analysis: first, fine-resolution environmental variables generally outperform

taxonomic indicators. Second, fine-resolution environmental indicators vary considerable between taxa and diversity components (richness, rarity, endemism and composition) in their representation power; on average they predict no more than 53% of the variation in local diversity patterns. Third, representation of species composition was remarkably lower compared to representation of species richness, rarity and endemism. The arthropod taxa sampled collectively represent the three most diverse terrestrial animal groups (insecta, arachnida, and diplopoda), allowing a broader taxonomic perspective than is usually applied in biodiversity indicator studies (Hilty and Merenlender, 2000). The breadth of phylogenies, trophic levels, and life history strategies of the taxa sampled further add to the representativeness of our results and potential applicability to other small-bodied organisms in the ecosystem. The poor performance of coarse-resolution habitat classification demonstrates that much of the variability in local scale diversity patterns lies within, rather than between, habitats. Hence, localscale conservation decision-making needs to take into account intra-habitat patchiness and variability as major drivers of diversity, not only broad habitat classes, as emphasizes by recent work (e.g. Gardner et al., 2008; Kati et al., 2004). Furthermore, it is necessary to establish the indicative abilities of coarse-resolution habitat types before applying them in local-scale conservation planning and management, as their performance may vary considerably according to spatial and ecological resolution; in our study region some correlations were found between habitat classifications and

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177

Table 6 Cross-taxon correlations in species composition. Values presented are correlation r2 between pairs of similarity matrices. Significance values were calculated with Mantel tests.

Plants Beetles Moths Spiders Scorpions Diplopods Mammals Reptiles Overall compositiona a ** ***

Plants

Beetles

Moths

Spiders

Scorpions

Diplopods

Mammals

Reptiles

– 0.25*** 0.23*** 0.11*** 0.14*** <0.01*** 0.05*** 0.01*** 0.41***

– 0.26*** 0.13*** 0.04*** 0.01*** 0.12*** 0.008** 0.14***

– 0.10*** <0.01** 0.01** 0.08*** <0.01** 0.11***

– 0.02*** <0.01** 0.12*** 0.03*** 0.03***

– <0.01*** 0.02*** <0.01** <0.01**

– 0.03*** 0.01** <0.01***

– <0.01** 0.02***

– <0.01**

Overall composition: all taxa combined excluding the indicator taxon. p < 0.01. p < 0.001.

plant diversity at coarser resolutions (Savoray, 1996; Shoshany et al., 1996). Fine-resolution environmental indicators outperform taxonomic indicators, especially in reflecting richness, rarity and endemism patterns. However, there are two major pitfalls in the application of fine-resolution environmental indicators, first, the high variability in their representation levels may limit their cost-effectiveness as different indicators will be needed for representation of different target taxa and different diversity components (Mandelik et al., 2010). Second, their overall representation power might be too limited for some conservation targets (Lovell et al., 2007). The limited performance of fine-resolution environmental indicators in some of the models may stem from: (a) non-linear or symmetrical effects of the structural and floristic characteristics investigated on species distribution (Araújo et al., 2003); (b) lack of equilibrium with current species distributions, e.g. species do not fully occupy all adequate niches (Araújo et al., 2008); and/or (c) predominance of non-environmental factors, such as historical effects, in shaping current species distributions (Hortal et al., 2009). In either case, if high resolution diversity mapping is required (>55% of the variation) the utility of environmental indicators is limited, as found in other studies dealing with diversity mapping (Hortal et al., 2009 and references therein), and with selection of complementary conservation networks (Rodrigues and Brooks, 2007). However, some local scale studies of small-bodied taxa did find supporting results for the use of environmental indicators (Antvogel and Bonn, 2001; Brin et al., 2009; Provencher et al., 2003; Ruggiero and Kitzberger, 2004). This variability in results indicates that the utility of fine-resolution environmental indicators is very much case specific and context dependent (Hess et al., 2006) and needs to be evaluated a priori. For high resolution diversity mapping in the studied ecosystem, fine-resolution environmental indicators cannot replace direct surveys of taxa. Different aspects of the vegetation (floristic vs. structural characteristics) varied in their contribution to the different models tested. Plant species richness was a prominent component of the environmental models for richness and of the taxonomic models for composition, though indication levels in the latter were limited (maximal r2 = 0.41). However, plant species richness had limited contribution if any to the other models: environmental models for rarity, endemism and composition, and taxonomic models for richness, rarity and endemism. Structural aspects of the vegetation of known effect on faunal diversity (Lawton, 1983), mainly abundance of different vegetation components, but also to some extent ground habitat and layering complexity, had an important contribution to all environmental models, especially the richness and composition. Hence, while plant species richness contributed to some diversity models, it needs to be combined with structural aspects of the vegetation in order to gain better prediction power and encompass wider aspects of diversity.

Variation between models (both environmental and taxonomic) in reflecting different diversity components was high. Species richness, rarity, and endemism were similarly represented (in terms of average r2 values) in the environmental models, while species composition had markedly reduced representation; average r2 values of the composition models were 36% lower than those of the richness, rarity and endemism models. A similar pattern of reduced species composition representation compared to species richness and rarity were found for the environmental models (using the “overall composition” values, as they compile all taxa and thus represent highest representation values; see Table 6). Though the statistical approach applied for the richness, rarity and endemism analyses vs. the composition analyses was different, we found similar patterns when all components were similarly analyzed (using the PCA factors for all analyses; Mandelik, 2005). Hence, the differences in representation level are probably caused not by mere analytical differences but by the inherent difference in the complexity of the different diversity measures. Species composition encompasses much ecological information and variation components (species richness, identity, abundance) compared to the simple richness, rarity and endemism variables. Species composition data is of high conservation value, especially at the local scale. Our results show that the representation of composition patterns is a major pitfall in the application of indictors for local scale diversity assessment; the maximal representation of composition variation achieved was 41%, and in most models remarkably lower. Hence, species composition needs to be evaluated separately in choosing indicators for diversity assessments and in conservation decision making as a whole. Our analyses show that the utility of biodiversity indicators for mapping local diversity patterns in the studied ecosystem depends on the resolution required. If general indication of diversity patterns is sought (e.g. for initial assessments of the area, for some long-term monitoring programs) fine-resolution environmental indicators are a suitable surrogate. However, acquiring high resolution distributional data on taxa cannot be achieved by applying either environmental or taxonomic indicators, and requires surveying the taxa of interest directly. These results should apply to other Mediterranean ecosystems, as we addressed main spatiotemporal variation factors and obtained comprehensive data sets (Mandelik, 2005). The effectiveness of the sophisticated planning and management tools now available is at first determined by the quality of their input data. Hence, higher investment in surveys is needed to enable efficient planning and management of biodiversity (Mandelik et al., 2005). Nevertheless, intra-taxon surrogates for species patterns, particularly the higher taxon approach (use of genera, families and higher taxonomic levels as surrogates of species; Mandelik et al., 2007) and morphspecies (classification of specimens by non-experts taxonomists based on readily identified morphological characteristics; Hall, 2005; Oliver and Beattie, 1996),

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as well as suites of indicators (Mandelik et al., 2010), were found effective for representing species patterns in the studied ecosystem while reducing the costs incurred. Resources thus saved can be invested in broadening the taxonomic, spatial, and temporal scope of surveys and overall improving the representation of biodiversity. Acknowledgements We thank A. Landesman for his help in field work, Y. Waisel and Y. Tankus for their assistance in plant identification, L. Friedman for his assistance in the identification of beetles, and the thoughtful comments of two anonymous reviewers who helped improve this manuscript. This research was supported by the Israeli Ministry of the Environment (Grant 706-2) and the Jerusalem Institute for Israel Studies.

Table A1 Results of Mantel tests for the correlation between species composition (using the Sorensen quantitative similarity index) and habitat structure (using Euclidean distances based on PCA factors) using only the 25 plots that were >350 m from a road. Taxon

r2

Z

p

Beetles Spiders Scorpions Diplopods Mammals Reptiles

0.17 0.19 0.01 0.03 0.16 0.01

514 806 238 503 500 546

<0.001 <0.001 0.002 <0.001 <0.001 <0.001

Appendix A. Composition analyses performed only with the 25 plots that were >350 m from a road, to explore possible effects of roads on indicators’ performance (Tables A1 and A2).

Table A2 Cross-taxon correlations in species composition using only the 25 plots that were >350 m from a road. Values presented are correlation r2 between pairs of similarity matrices. Significance values were calculated with Mantel tests.

Plants Beetles Spiders Scorpions Diplopods Mammals Reptiles Overall compositiona a ** ***

Plants

Beetles

Spiders

Scorpions

Diplopods

Mammals

Reptiles

– 0.23*** 0.06*** 0.12*** <0.01*** 0.10*** <0.01*** 0.44***

– 0.04*** 0.06*** 0.01*** 0.14*** <0.01** 0.08***

– 0.04*** <0.01** 0.1*** 0.01*** 0.04***

– <0.01*** 0.02*** <0.01** <0.01**

– 0.03*** <0.01** 0.03***

– 0.01*** 0.03***

– 0.04***

Overall composition: all taxa combined excluding the indicator taxon. p < 0.01. p < 0.001.

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