Aquatic Botany 101 (2012) 1–7
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Can macrophytes be a surrogate for amphibians and physico-chemical features in pond classifications? Marco Landi a,b,∗ , Sandro Piazzini b , Alessia Nucci b , Carlo Saveri a , Claudia Angiolini b a b
Ufficio Territoriale per la Biodiversità di Siena, Corpo Forestale dello Stato, Via Cassia Nord 7, 53100 Siena, Italy Department of Environmental Science “G. Sarfatti”, University of Siena, Via P.A. Mattioli 4, 53100 Siena, Italy
a r t i c l e
i n f o
Article history: Received 19 September 2011 Received in revised form 23 February 2012 Accepted 8 March 2012 Available online 16 March 2012 Keywords: Aquatic vegetation Classification strength Community Concordance Italy
a b s t r a c t Our primary goal was to test how consistently macrophytes, physico-chemical features and amphibians classify pond sites, by applying a measure of classification strength based on a set of cross-tests performed with randomisation protocols. Finally, we used ordination methods to identify the major environmental factors correlated with each biotic group. Significant results of concordance and higher values of relative classification strength were obtained at two (or more) cut levels, when the plant classification was performed on amphibians and on physico-chemical characteristics. Significant results and higher values of relative classification strength were also obtained at a cut level when the amphibian classification was performed on physico-chemical features. The ordination analyses revealed that plants and amphibians were affected by the same pond features, mainly conductivity, size and depth. Ponds with high conductivity were dominated by tall emergent plants of the genus Typha and were the preferential sites for Bufo bufo. Smaller shallow ponds with small emergent plants seemed instead to favour Rana dalmatina. Deep ponds with low conductivity were mostly occupied by floating and submerged plants, such as Potamogeton natans and Chara hispida, and hosted newts (Triturus carnifex and T. vulgaris), probably because the latter depend on well structured vegetation with submerged plants for egg deposition. These results suggest that pond ecosystems have “two levels of influence”, and that plants are the “middle level” between environmental features and amphibian assemblages, since they are directly influenced by the former and directly influence the latter. It is probably by virtue of this intermediate position that the classification of ponds based on plant assemblages can be used as a surrogate for predicting environmental features and the presence of amphibian species of conservation interest, in order to preserve their habitat through preliminary and cost-effective assessments. Given the ongoing threats to ponds, these findings are important for their protection, and better understanding of the ecological preferences of various plant and amphibian species is useful for planning management and conservation strategies. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Concordance among communities measures the degree to which patterns in community structure in a set of sites are similar among different taxonomic groups (Paszowski and Tonn, 2000). When there is concordance, a well-known key group may be used as surrogate to indicate patterns of the other taxonomic groups (Paavola et al., 2003). Despite its obvious importance, community concordance has been relatively little studied in aquatic ecosystems. These studies have yielded variable results, although relatively strong concordance has been observed among such divergent groups as benthic
∗ Corresponding author at: Department of Environmental Science “G. Sarfatti”, University of Siena, Via P.A. Mattioli 4, 53100 Siena, Italy. Tel.: +39 0577 235408; fax: +39 0577 232896. E-mail address:
[email protected] (M. Landi). 0304-3770/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.aquabot.2012.03.003
macroinvertebrates and fish (Bowman et al., 2008; Jackson and Harvey, 1993), and aquatic birds and fish (Paszowski and Tonn, 2000), while studies have only rarely included macrophytes, which are generally used as a measure of species richness (Bagella et al., 2011; Dias et al., 2011; Heino, 2002), despite the importance of their cover as a habitat for invertebrates and vertebrates (Hartel et al., 2009; Strayer and Malcom, 2007). In fact, in wetland ecosystems, plants provide food and shelter for other organisms that live in the water or spend only part of their lives in the water, such as amphibians. Plants are in close contact with these organisms through their root systems and especially leaves, which may be submerged, floating or emergent. Moreover, in many studies plants have proven to be good indicators of the environmental characteristics of a whole aquatic ecosystem, indeed, a good correlation was recently found between patterns of plant and aquatic or semi aquatic animals (Gioria et al., 2010; Heegaard et al., 2001). While a single amphibian species, with distinct ecological characteristics, can be used as indicator of aquatic plant diversity (Gustafson et al.,
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2006), different species of amphibians may show different degrees of preference for a particular aquatic plant assemblage (Santi et al., 2010). On this basis, a classification of ponds based on aquatic vegetation may provide a useful picture of the variability of amphibian species and environmental factors and vice versa, so that the two classifications could be used as surrogates of each other. Environmental characteristics have in fact been demonstrated to affect both vegetation and fauna (see Bagella et al., 2010; Della Bella et al., 2008; Seminara et al., 2008). Congruent patterns among taxonomic groups can mainly arise from similar but independent responses by various taxonomic groups to the main environmental gradients (Paavola et al., 2003). Such shared responses are ecologically interesting because they suggest that taxonomically divergent groups are controlled by relatively few environmental factors. Clearly, identifying such key factors would be of considerable value for integrated management and conservation of aquatic biota. Despite the importance of this topic, few studies on community concordance in aquatic ecosystems have provided good results and concordance has generally proven stronger among communities occupying lower trophic levels (Paszowski and Tonn, 2000). This is particularly true when many variables affect relationships between different groups of taxa: for example, plant and animal communities of ponds are often uniquely adapted to a substrate that may vary from submerged to dry, while rate of filling and drying can depend on environmental factors and management related to human activity. Moreover, aquatic plant communities are grazed by fish and birds, and individual plants resist such disturbance differently (Hidding et al., 2010). Larval amphibians are vulnerable to aquatic predators and predator densities are often higher in permanent than temporary ponds (Kats et al., 1988; Woodward, 1983). Different kinds of landscape surrounding ponds can also be determinant for certain amphibian species (Cheruvelil and Soranno, 2008; Hartel et al., 2009). In the present study, we examined the concordance between two different biotic communities (aquatic plants and amphibians) and environmental features of semi-permanent ponds in central Italy. To overcome these problems, we chose ponds in a protected area with a homogeneous landscape (consisting of forests and small meadows), where fish were absent (to avoid macrophyte grazing and predation of eggs and larvae). Our primary goal was to test how consistently the three different groups classified pond sites. Our specific question was: can site groupings based on aquatic plants be used as a surrogate for classifying amphibian assemblages or physico-chemical variables? For this purpose, we applied site classifications separately for plant assemblages, amphibians and environmental variables. These types were then cross-tested on the other two biotic groups using randomisation protocols. Finally, we used ordination methods to identify the major environmental factors correlated with each biotic group, assessing whether the groups responded differently to pond features. In this way, we aimed to acquire a better understanding of the ecological preferences of various plant and amphibian species, for use as a basis for pond management in protected areas.
2. Materials and methods 2.1. Study area We sampled 26 semi-permanent ponds (which dry up only in exceptionally arid years), located in a protected area (Natural State Reserve “Cornocchia”) in Tuscany (central Italy) (670,769 east; 4,789,325 north, UTM WGS84). These Mediterranean ponds are close to each other, included in an area of 30 km2 , and have similar physical features of catchment area (i.e. soil, water supply,
rainfall and temperature) with the exception of size and depth. Their areas are less than 0.1 ha and their altitudes between 310 and 590 m a.s.l. The surrounding vegetation is mainly forest and partly meadow. The study area is sparsely populated with isolated farmhouses and small villages but no major industrial settlements. Based on climatic data for the years 1992–2006 (Pentolina station, ARSIA), mean total annual rainfall is 798 mm, mean annual temperature 13.5 ◦ C and July is the driest month. 2.2. Field sampling and data collection Twenty-six ponds lacking fish were investigated and each was regarded as a single sampling unit. Sampling was done twice for amphibians during the breeding season (April–May and June–July 2009), and once for plants and physico-chemical features in June–July 2009. The biological and chemical data of each pond was obtained in the water, where most of the biomass occurred (depth 0–50 cm). When it was not possible to observe submerged vegetation (for example due to an abundance of floating leaves), the percentage cover of submerged plants was estimated using a viewing aid (glass-bottomed bucket). Three data sets were recorded as follows: (1) cover of each species of vascular plant and algae belonging to the Characeae family (estimated by two observers), expressed as a percentage of pond surface area, considering the whole surface of the pond simultaneously; (2) abundance of each amphibian species, sampled by visual encounter survey and dipnetting with effort (total sampling time) proportional to pond size (about 2 min per 5 m2 ); (3) physico-chemical characteristics: pond surface area (m2 ), calculated as a circle using the average radius (estimated in the field using a tape measure), since all ponds were nearly circular in shape; maximum pond depth (m) measured in the middle (5 or more readings were taken) with a graduated stick; average slope (%), obtained from the relationship between maximum depth and average radius of the pond; conductivity (mS/cm) and pH of water obtained from the mean of two samples measured with multi-parameter Eutech PC 650 instruments on opposite sides (depth 0–50 cm) of each pond. All the physico-chemical characteristics were measured (once a year on the same day) during summer plant sampling. The nomenclature of plant species follows Conti et al. (2005) and that of amphibian species follows SHI (2007). Rana bergeri and Rana klepton hispanica were grouped together as a single unit (Rana synklepton hispanica). 2.3. Data analysis The test of concordance was achieved by three main steps. As first step of data analysis, we grouped ponds on the basis of plant, amphibian and physico-chemical data, so that all further analysis could proceed at group level. To do this, the three data sets underwent hierarchical cluster analysis. As suggested by McCune and Grace (2002), the Bray–Curtis dissimilarity index and flexible beta (ˇ = −0.25) clustering, which should perform largely similar to Ward’s linkage but is not limited to Euclidean distance, were used for biological data, while Euclidean distance and Ward’s linkage were used for physico-chemical data. Thus we obtained: (1) a classification based on plant assemblages, (2) a classification based on amphibians and (3) a classification based on physico-chemical features. In the second step, we used Multiple Response Permutation Procedures (MRPP) to test the performance of each classification applied to the other two data sets. To do so, the cluster groups were cross-tested on the other two data sets and accepted only if significant (P < 0.05). We also used MRPP analysis for a posteriori classification (self-test) to obtain the “best possible” values of these statistics to use for numerical comparison with values of a priori classification (cross-test).
M. Landi et al. / Aquatic Botany 101 (2012) 1–7
MRPP is a data-dependent permutation test, mathematically akin to analysis of variance, as it compares dissimilarities within and between groups, but has the advantage of requiring few assumptions about the distribution structure of the data. This makes it ideal for testing between-group differences in ecological data, even if the number of cases differs between groups. MRPP consists of two statistical tests: the A statistic estimates within-group homogeneity and the T statistic measures between-group separability. Higher values of the A statistic (maximum value = 1) indicate a high degree of homogeneity within groups, while a large negative T value (≤−10) indicates high separability between groups. Moreover, A = 0 when within-group community heterogeneity equals that expected by chance and A < 0 when heterogeneity exceeds that expected by chance (McCune and Mefford, 1999). The null distribution of the test statistic (chance-corrected within-group similarity, A) is based on the collection of all possible permutations of the objects into groups of a specified size. In MRPP, the null hypotheses of no differences among groups were assessed by a Monte Carlo permutation procedure with 999 permutations. In the third step, we also used the classification strength (CS) approach of Van Sickle and Hughes (2000) to obtain a single measure of overall classification strength. CS is based on comparison of the mean of all between-class similarities (B) and mean within-class similarity (W). We calculated B and W using Bray–Curtis dissimilarity for biological data and Euclidean distance for physico-chemical data. CS is defined as the difference between these similarities (CS = W − B) and ranges from zero to one, values near zero indicating that sites are randomly assigned to classes. The classification strength of each cross-test was compared with the classification strength of a posteriori classification. Thus, a posteriori classification was used as a standard, representing the near-maximum classification strength attainable. Relative classification strength has been proposed as a means to compare the strengths of classifications. It is the ratio of absolute CS associated with an a priori classification to the absolute CS for an a posteriori classification, and is expressed as a percentage (%). Finally, Non-Metric Multidimensional Scaling (NMDS) based on Bray–Curtis dissimilarity was used to investigate the relationships between community patterns and to identify the physico-chemical factors contributing to these patterns. NMDS was run on a plant species matrix consisting of their cover values in 26 ponds, while amphibian species (as abundance values) and physico-chemical factors were fitted as vectors. A three-dimensional solution was recommended on the basis of a scree plot of the number of dimensions vs. stress. All analysis was performed using PcOrd (McCune and Mefford, 1999). Prior to analysis, all data was log(x + 1) transformed. 3. Results In the study area, 26 species of vascular plants, one green alga (Chara hispida) and 7 species of amphibians were recorded. The mean ±SD number of macrophytes (including genus Chara) per pond was 1.7 ± 4.5, while for amphibians it was 3 ± 1.2; at least one macrophyte and amphibian species was found in all ponds. The descriptive statistics of the physico-chemical variables are summarised in Table 1. 3.1. Cluster concordance The ponds were grouped into clusters at different cut levels (degree of similarity or distance). The dendrograms were cut at each node of the hierarchy, stopping at the level where the resulting groups still contained a sufficient number (at least 2) of ponds to characterise the groups. Each classification had three cut levels
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Table 1 Descriptive statistics of physico-chemical variables including mean, standard deviation (S.D.), minimum (Min) and maximum (Max).
Conductivity (S/cm) pH Size (m2 ) Slope (%) Depth (m)
Mean
S.D.
Min
Max
474 7.9 351 16.4 1.3
333 0.7 249 12.2 0.7
90 7 71 2 0.2
1331 10 989 51 2.5
and two, three and four distinct groups were obtained. The dendrograms were scaled according to the percentage of information remaining in the analysis, where less information remaining indicated a weaker association between ponds. All information was included when individual ponds were considered and no information remained when all ponds were combined into a single group. For each cut level we found a total residual information that had quite similar values among the three cut levels of different classifications (Table 2); lower values were obtained for ponds classified according plant species. In line with observations in previous studies (Paavola et al., 2003), each data set gave the best results under a posteriori classification (self-test) than under a priori classification (crosstest). Considering the cross tests, significant concordance was obtained at all cut levels (two, three and four groups, respectively) with the plant species classification performed on amphibians, and the third level (four groups) when performed on physicochemical characteristics (Table 3). Other significant results were obtained at the second and third cut level (three and four cluster groups, respectively) with the classification of amphibian species and physico-chemical features performed on plant species, and only the third level (four groups) with the amphibian classification performed on physico-chemical features and vice versa (Table 3). When we only considered the cross tests with significant results according to MRPP analysis, higher relative classification strengths were obtained with: plant species classification performed on amphibian species (relative CS = 30–38%) and on physico-chemical features (relative CS = 90%); amphibian species classification performed on plant species (relative CS = 24–25%) and on physico-chemical features (relative CS = 88%) (Table 4). In all cases, physico-chemical features classification showed weak relative classification strengths (relative CS = 8–19%) with the other two data sets (Table 4). 3.2. Ordination analysis The NMDS results provided a three-dimensional representation with a statistically significant reduction in stress (final stress = 9.903, P-value = 0.0323). The first three axes explained 93% of community variation (axis 1 captured 31% of the variance, axis 2, 36% and axis 3, 26%). Biplots of NMDS are shown for first, second and third axes (Fig. 1a and b) with ponds grouped in the four groups obtained from the third level of the classification carried out on plant species. The placement of amphibian species and physico-chemical features as vectors in the ordination indicates the Table 2 Percentage of information remaining in the classifications at the three cut levels. Cut levels
1 2 3
No. of groups
2 3 4
Information remaining (%)
Plants
Amphibians
Physicochemical
38.5 51.1 62.5
38.5 58.1 68.0
42.2 57.5 67.5
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Table 3 Results of the cross-test based on Multiple Response Permutation Procedures (MRPP) of classifications performed on other data sets. The rows define the clusters and columns define the data sets that were tested against those clusters. Significant results are given in bold. A statistic of MRPP is given for each pair tested. Self-tests performed with a posteriori classification (in italics), for comparison of values, are also shown. Classification
Plants Amphibians Physico-chemical Plants Amphibians Physico-chemical Plants Amphibians Physico-chemical
No. of groups
Plants
2 2 2 3 3 3 4 4 4
Amphibians
A
T
P-value
A
0.152 0.018 0.010 0.228 0.080 0.111 0.296 0.101 0.155
−9.542 −0.884 −0.631 −9.507 −2.954 −4.577 −9.051 −2.930 −5.079
– 0.178 0.222 – 0.008 0.001 – 0.006 0.001
0.085 0.105 0.026 0.087 0.368 0.066 0.117 0.417 0.096
Physico-chemical T
−4.64 −8.10 −1.396 −3.138 −11.77 −2.269 −3.101 −10.55 −2.74
P-value
A
0.001 – 0.094 0.008 – 0.051 0.009 – 0.014
0.002 0.021 0.123 0.043 0.041 0.260 0.161 0.097 0.345
T −0.115 −1.130 −7.180 −1.519 1.276 −10.01 −4.102 −2.429 −10.62
P-value 0.353 0.130 – 0.079 0.108 – 0.001 0.017 –
Table 4 Absolute and relative classification strengths (CS). Relative CS are calculated from the absolute CS (numbers in italics) of the a posteriori classification. Classification
No. of groups
Plants
Amphibians
Physico-chemical
Absolute CS
Relative CS (%)
Absolute CS
Relative CS (%)
Absolute CS
Relative CS (%)
Plants Amphibians Physico-chemical
2 2 2
0.262 0.112 0.043
– 20 11
0.099 0.547 −0.010
38 – –
0.154 0.128 0.380
59 23 –
Plants Amphibians Physico-chemical
3 3 3
0.322 0.111 0.132
– 25 18
0.096 0.450 0.047
30 – 6
0.288 0.331 0.752
89 74 –
Plants Amphibians Physico-chemical
4 4 4
0.364 0.095 0.157
– 24 19
0.111 0.396 0.069
30 – 8
0.327 0.350 0.820
90 88 –
Table 5 Pearson product-moment correlation coefficient between plant species, amphibian species and physico-chemical variables with the first three axes of NMDS. Other plant species not mentioned in Table 1 were: Agrostis stolonifera, Bolboschoenus maritimus, Carex distans, Juncus inflexus, Lycopus europaeus, Lythrum salicaria, Nymphaea (cultivar), Persicaria hydropiper, Phalaris caerulescens, Phragmites australis, Potamogeton trichoides, Schoenoplectus lacustris, Scirpoides holoschoenus, Sparganium erectum ssp. neglectum, and Xanthium orientale ssp. italicum. Plant species
Axis 1
Axis 2
Axis 3
Alisma plantago-aquatica Chara hispida Eleocharis palustris Groenlandia densa Juncus articulatus Juncus bufonius Mentha aquatica Potamogeton natans Ranunculus trichophyllus Typha angustifolia Typha latifolia Zannichellia palustris ssp. polycarpa
0.308 0.187 −0.781 0.084 0.511 0.156 0.098 −0.111 −0.281 0.495 0.429 0.422
−0.020 −0.060 −0.543 −0.044 −0.016 0.264 −0.149 0.612 0.243 0.224 0.291 −0.116
0.117 −0.819 0.246 −0.356 0.452 0.615 −0.046 −0.636 0.136 0.266 0.009 0.268
Amphibian species Triturus carnifex Triturus vulgaris Bufo bufo Hyla intermedia Rana dalmatina Rana synklepton hispanica Physico-chemical features Conductivity pH Size Slope Depth
Examination of the NMDS ordination with cluster groups (Fig. 1a and b) and correlation coefficient (Table 5) indicated that: axis 1 and 3 were positively associated with Cluster group 1 and correlated with plants such as Typha angustifolia, T. latifolia, Juncus articulatus and Zannichellia palustris ssp. polycarpa, anuran amphibians such as Bufo bufo and linked to high conductivity; axes 1 and 2 were negatively and positively associated (respectively) with Cluster group 2 and correlated with plants such as Potamogeton natans and Ranunculus trichophyllus; axes 1 and 2 were negatively associated with Cluster group 3 and correlated with Eleocharis palustris, amphibian such as Rana dalmatina and linked to low size, slope and depth; axis 3 was negatively associated with Cluster group 4, characterised by assemblages of plants such as P. natans and C. hispida, urodel amphibians such as Triturus carnifex and T. vulgaris and linked to deep ponds. Moreover, along axis 2, Rana synklepton hispanica was separated from R. dalmatina. Hyla intermedia and pH showed no relationships with the ordination axes (Table 5). 4. Discussion
0.152 0.044 0.460 0.166 0.084 0.140
−0.081 0.006 0.140 −0.055 −0.377 0.204
−0.356 −0.635 0.374 0.177 −0.055 −0.077
0.523 −0.133 0.207 0.300 0.282
0.010 0.085 0.456 0.544 0.634
0.039 −0.109 0.021 −0.319 −0.350
relationship between these variables and cluster groups. The correlations between the first three axes of NMDS and variables are shown in Table 5.
Landscape classifications are increasingly being used in conservation planning and biodiversity management, although biotic assemblages often show statistically significant differences between landscape classes, albeit with rather weak classification strengths (Hawkins et al., 2000; Johnson and Host, 2010). According to Hawkins et al. (2000), divisions are considered to performed poorly when CSs < 0.20. In the present study, concordances greater than 0.20 were observed when the classification of plants and amphibians was performed on physico-chemical features. However, there may be many reasons for high values of CS. Firstly, the predictivity of a classification may be influenced by the number of samples (ponds in our case). When the number of ponds increases, both W and B decrease, probably in relation to increased total biotic heterogeneity occurring as new sites are added to the classification, causing classes to be more similar to each other (Hawkins
M. Landi et al. / Aquatic Botany 101 (2012) 1–7
Fig. 1. Ordination diagrams for NMDS analysis of ponds (n = 26) run on plant species matrix with projected amphibian species (grey vectors) and physico-chemical features (dark vectors). The final solution was three-dimensional. Plots of NMDS axes 1 and 2 (a) and axes 1 and 3 (b) are shown. The four groups obtained from the third level of classification are shown by numbers. Amphibian abbreviations are the first three letters of genus and species (see Table 5). Vector length is proportional to the correlation coefficient.
and Vinson, 2000). Moreover, for the a priori classifications, random sampling effects may be responsible for the high variability in estimates of W, and this effect is greater with small sample size. Secondly, most previous studies on this topic have analysed macroinvertebrates and riparian ecosystems (Hawkins and Vinson, 2000; Heino and Mykrä, 2002; Paavola et al., 2003). When studying macroinvertebrates, many authors found that the cause of poor classification performance could be due to the widely distributed taxa. Taxa occurring nearly everywhere may obscure real biological differences between sites, promoting relatively large values of B (Hawkins et al., 2000; Olivier et al., 2004). In our case, on the contrary, most of these amphibian species occupied smaller areas, some probably also competing with and excluding each other (Bardsley and Beebee, 2000; Van Buskirk, 2007; Vignoli et al., 2009). Moreover, the dynamic nature of riparian contexts causes high
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environmental heterogeneity, that in turn decreases within-group homogeneity and between-group differences, leading to poor classification strength (Gregory et al., 1991). From this point of view, ponds are more stable and the environmental differences between them are sharper than between different stretches of a river (Santi et al., 2010). The amount of information remained quite similar among the same levels of different classifications. However, lower values for plant classification showed a weaker association between ponds classified on the basis of plants than on the basis of physicochemical features or amphibian species. Significant concordance and higher relative CS were obtained at two (or more) cut levels with the plant classification performed on amphibians and on physico-chemical characteristics. Significant concordance and higher relative CS were also obtained at one cut level with the amphibian classification performed on physico-chemical features. In any case, the lack of studies using macrophytes and amphibians for pond classification concordance precludes a wider comparison, and any explanations of responses or lack thereof are necessarily tentative. A strong correspondence between plant classification and environmental variables emerges from previous studies (Hrivnák et al., 2006), but also the contrary, since the presence of plants may also affect certain features of water (e.g. nutrients and redox potential, see Boros et al., 2011; Granéli and Solander, 1988). However, plant assemblages in turn strongly affect the amphibians living in these ponds, as demonstrated by the strength of the plant classification applied to the amphibian database. In fact, plants are of fundamental importance for the survival of amphibians, providing food, shelter and support for eggs during reproduction. These findings suggest that our system has “two levels of influence”, and that plants are the “middle level” between physico-chemical features and amphibian assemblages, being directly influenced by the former and directly influencing the latter. By virtue of this intermediate position, the classification of ponds based on plant assemblages can probably act as a surrogate, accounting simultaneously for much of the variability of the other two levels. The results of the ordination showed that aquatic plants and amphibians were affected by the same features, mainly conductivity, size and depth, as already observed by other authors (Bilton et al., 2001; Spencer et al., 1999). Moreover, in our study, emergent vegetation cover was found to be an important predictor for amphibians, as found by Santi et al. (2010), and our results on pond selection by amphibian species here analysed are in line with the findings of previous studies on this topic (Ficetola and De Bernardi, 2004; Hartel et al., 2009; Skei et al., 2006). In fact, shallow ponds with high conductivity were dominated by tall emergent plants, such as the genus Typha, in line with Joniak et al. (2007) and Steinbachová-Vojtíˇsková et al. (2006), and were the preferential sites for B. bufo. Indeed, this anuran species uses emergent plants as a substrate for spawning and does not require oligotrophic water (Hartel, 2004; Hartel et al., 2008). Moreover, B. bufo can even spawn in ponds that dry up, because it lays its eggs in late winter and the larvae develop by June, before all the water evaporates (Sindaco et al., 2006). Smaller shallow ponds with small emergent plants, such as E. palustris, seemed to favour R. dalmatina. Since these ponds represent an early successional stage of vegetation, they are preferential sites for breeding and egg-laying of R. dalmatina (Hartel, 2004; Hartel et al., 2009; Vignoli et al., 2009). Deep ponds with low conductivity were mostly occupied by plants with floating leaves, such as P. natans, and submerged leaves, such as C. hispida, and turned out to host Triturus species, probably because the latter are strongly linked to well structured vegetation with submerged plants for egg deposition (Gustafson et al., 2006; Pavignano et al., 1990; Skei et al., 2006). Oldham et al. (2000) also observed that Triturus cristatus occupies ponds with emergent plant cover between
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25 and 50% and submerged plants between 50 and 75%. The tendency for both newt species to occur in the same pond indicates a high degree of similarity in habitat requirements. Moreover, the depth of ponds is one key factor for these amphibians, whose limiting factor for survival is sufficient water in the driest months (see also Ficetola and De Bernardi, 2004; Oldham et al., 2000). The fact that pH showed no linkages with amphibian species is not surprising, because our ponds all showed pH values between 7 and 10, while newts also tolerate pH close to 4–5 (Griffiths, 1994). Other authors also found that pH influenced both species of Triturus in a convex nonlinear fashion with the highest probability of presence around pH 6.5 (Skei et al., 2006), while acidic pond water has been found to adversely affect amphibians (Freda, 1986). This suggests that newt species may be highly vulnerable to natural or man-made changes that influence the ionic concentration of the ponds they inhabit (Skei et al., 2006), thus underlining the importance of maintaining a natural terrestrial buffer zone around wetlands in order to conserve amphibian populations (e.g. Porej et al., 2004; Semlitsch and Bodie, 2003). The species H. intermedia showed no linkages with the variables here analysed. Other authors (Ebisuno and Gentilli, 2002; Ficetola and De Bernardi, 2004) found that differences in distribution of H. intermedia with respect to other amphibian species may be due to its preference for sunny habitats, as a thermophilic species, although in the present study this preference was impossible to detect, since light intensity was not among the variables considered. The ecological preferences emerging in our study proved to agree with the results of classification, and together underlined the importance of pond vegetation and its potential as a surrogate for predicting environmental features and the presence of amphibian species of conservation interest, in order to preserve their habitat through preliminary and cost-effective assessments. Plant assemblages can therefore identify ponds requiring specific management (e.g. maintenance of depth of small ponds for submerged and floating-leafed plants, as well as for newts). Given ongoing threats to ponds, these findings are important for protection of these habitats, allowing better planning of management and conservation strategies. Further detailed studies into the links between plants, amphibians and environmental features of ponds are however needed. Acknowledgements We thank the staff of the Ufficio Territoriale per la Biodiversità di Siena – Corpo Forestale dello Stato for their help with field work. References Bagella, S., Gascón, S., Caria, M.C., Sala, J., Mariani, M.A., Boix, D., 2010. Identifying key environmental factors related to plant and crustacean assemblages in Mediterranean temporary ponds. Biodivers. Conserv. 19, 1749–1768. Bagella, S., Gascón, S., Caria, M.C., Sala, J., Boix, D., 2011. Cross-taxon congruence in Mediterranean temporary wetlands: vascular plants, crustaceans, and coleopterans. Community Ecol. 12, 40–50. Bardsley, L., Beebee, T.J.C., 2000. Competition between Bufo larvae in a eutrophic pond. Oecologia 124, 33–39. Bilton, D.T., Foggo, A., Rundle, S.D., 2001. Size, permanence and the proportion of predators in ponds. Arch. Hydrobiol. 151, 451–458. Boros, G., Søndergaard, M., Takács, P., Vári, A., Tátrai, I., 2011. Influence of submerged macrophytes, temperature, and nutrient loading on the development of redox potential around the sediment–water interface in lakes. Hydrobiologia 665, 117–127. Bowman, M.F., Ron, I., Reid, R.A., Somers, K.M., Yan, N.D., Paterson, A.M., Morgan, G.E., Gunn, J.M., 2008. Temporal and spatial concordance in community composition of phytoplankton, zooplankton, macroinvertebrate, crayfish, and fish on the Precambrian Shield. Can. J. Fish. Aquat. Sci. 65, 919–932. Cheruvelil, K.S., Soranno, P.A., 2008. Relationships between lake macrophyte cover and lake and landscape features. Aquat. Bot. 88, 219–227. Conti, F., Abbate, G., Alessandrini, A., Blasi, C., 2005. An Annotated Check-list of the Italian Vascular Flora. Palombi & Partner, Roma.
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