Butterfly diversity along the urbanization gradient in a densely-built Mediterranean city: Land cover is more decisive than resources in structuring communities

Landscape and Urban Planning 183 (2019) 79–87

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Research Paper

Butterfly diversity along the urbanization gradient in a densely-built Mediterranean city: Land cover is more decisive than resources in structuring communities

T

Olga Tzortzakakia, , Vassiliki Katib, Maria Panitsaa, Evangelos Tzanatosa, Sinos Giokasa ⁎

a b

Department of Biology, University of Patras, Patras, Greece Department of Biological Applications & Technology, University of Ioannina, Ioannina, Greece

GRAPHICAL ABSTRACT

ARTICLE INFO

ABSTRACT

Keywords: Urbanization gradient Lepidoptera Community structure Habitat Resource availability

Urbanization induces rapid landscape and habitat modifications leading to alterations in species distribution patterns and biodiversity loss. As pollinating insects such as butterflies are particularly susceptible to urbanization, it is important to pinpoint the factors that could enhance their diversity in the urban areas in order to design adequate management and conservation actions. Our study aims to investigate the influence of land cover and local habitat characteristics on the butterfly diversity patterns and community structure in a densely built city in the eastern Mediterranean region. We carried out butterfly surveys (line transects) in 45 randomly selected sites, distributed along an urbanization gradient. In each site, we assessed the surrounding landscape by measuring the land cover in a 200-m buffer zone, and the local habitat by estimating the available plant resources along each transect. Overall, 1805 individuals belonging to 41 butterfly species were recorded. Land cover was found to have the strongest influence on butterfly species richness, abundance and community structure. Although plant resources were sufficiently available within the whole study area, the butterfly community was significantly poorer in the more urbanized areas, indicating the potential role of habitat fragmentation and patch isolation. In contrast, butterfly diversity was significantly higher in the peri-urban area, underlying its conservation value for butterflies in the urban landscape. We attribute these findings to the degradation of the more urbanized areas due to long-term inadequate planning and the disorganized expansion of the city.

Corresponding author at: Department of Biology, University of Patras, GR-26504, Rio – Patras, Greece. E-mail addresses: [email protected] (O. Tzortzakaki), [email protected] (V. Kati), [email protected] (M. Panitsa), [email protected] (E. Tzanatos), [email protected] (S. Giokas). ⁎

https://doi.org/10.1016/j.landurbplan.2018.11.007 Received 22 November 2017; Received in revised form 4 May 2018; Accepted 28 November 2018 0169-2046/ © 2018 Elsevier B.V. All rights reserved.

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1. Introduction Global environmental changes such as habitat loss, fragmentation, agricultural intensification and climate change have been exerting strong pressures on biodiversity, altering species distribution patterns and community composition (Chapin et al., 2000). Urbanization is one of the most important causes of natural ecosystem loss and habitat fragmentation (Grimm et al., 2008) triggering species diversity decline (McKinney, 2008). Pollinating insects are considered among the groups most affected by urbanization, having experienced significant population declines in urban areas (Jones & Leather, 2012). Nevertheless, such areas are known to retain a great potential for biodiversity and thus, their role as refuges for insect pollinators needs to be reinforced (Hall et al., 2016). Hence, management actions oriented towards retaining important habitats (Leston & Koper, 2017; Werner, 2011) can increase the opportunities provided by urban areas for the conservation of insect populations in a rapidly changing and urbanizing world (UN, 2014). Butterflies are among the most susceptible wildlife groups showing rapid responses to environmental changes (Dennis, 2010) such as urbanization (Kitahara & Fujii, 1994). Characterized by a short life-cycle, food specialization and strong affinity to specific habitats during different stages of their life-cycle, they are considered suitable environmental indicators (Dennis, 2010; EEA, 2017). Furthermore, butterfly species that specialize in specific plant resources during their larval stage have been shown to be more affected by urbanization than generalist species (Bergerot, Fontaine, Renard, Cadi, & Julliard, 2010; Kitahara & Fujii, 1994). Hence, studies on urban butterfly communities can provide new insight into the way green spaces should be managed for the benefit of insect populations; this could lead to a re-evaluation of the prevalent traditional idea of creating tidy and homogenous green spaces with several non-native ornamental plants (Burghardt, Tallamy, & Shriver, 2009; Leston & Koper, 2017). To design adequate management strategies aiming to enhance butterfly diversity, it is essential to identify the ecological processes as well as the landscape and habitat characteristics that shape species distribution patterns and community composition. The effect of the interplay between landscape characteristics and the local habitat quality on butterfly diversity is still under discussion, as it may often vary among different urban habitats, cities or spatial scales within the same city (Clark, Reed, & Chew, 2007; Leston & Koper, 2017; Matteson & Langellotto, 2010; Ramírez Restrepo & Halffter, 2013; Soga et al. 2015; Wood & Pullin, 2002). Landscape structure in association with species dispersal ability determines the distributions of species populations (Snep et al., 2006; Wood & Pullin, 2002). Habitat diversity and connectivity, the presence of corridors, and herbaceous vegetation cover are deemed the most important landscape factors impacting on butterfly diversity (Beninde, Veith, & Hochkirch, 2015; Lizée, Manel, Mauffrey, Tatoni, & Deschamps-Cottin, 2012; Öckinger, Dannestam, & Smith, 2009). Butterfly communities might also show varying responses to local and landscape factors depending on the dominant land cover types in the study area (Soga et al., 2015). Moreover, butterflies are largely dependent on habitat quality and the availability of plant resources (Thomas et al., 2001), such as the abundance of larval hostplants, the availability of nectar resources (Bergerot et al., 2010; Yamamoto, Yokoyama, & Kawata, 2007) and the presence of herbs acting as shelter for both adults and larvae (Leston & Koper, 2017) for their survival and reproduction. Our study area is located in the eastern Mediterranean Basin, where long-term human presence has modified the landscape to a great extent (Blondel, Aronson, Bodiou, & Boeuf, 2010). In recent decades, several Mediterranean cities have experienced an intense urbanization process, mainly due to the abandonment of agricultural land and the augmentation of tourism infrastructure (Munoz, 2003), while local planning strategies were often ineffective in controlling the urban sprawl (Chorianopoulos, Pagonis, Koukoulas, & Drymoniti, 2010). Despite the possible impacts of the rapid urban expansion on wildlife, urban

Fig. 1. Distribution of sampling sites in the urban (red squares), suburban (blue circles) and peri-urban zone (black triangles). Land uses were derived from the Urban Atlas classification (EEA, 2010). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

biodiversity in the eastern Mediterranean remains poorly studied (Beninde et al., 2015; Ramírez-Restrepo & MacGregor-Fors, 2016). Our study aims to identify the overriding drivers of butterfly diversity patterns in a densely built Mediterranean city (Patras, Greece). Our objectives were to investigate: (1) how butterfly diversity patterns vary along the urbanization gradient; (2) the influence of land cover (a significant element of the adjacent landscape) and local habitat characteristics on butterfly diversity and community structure; and (3) the response of species with specialized larval feeding preferences to urbanization. 2. Methods 2.1. Study area and sampling sites The study was conducted in the city of Patras (38° 14′ N, 21° 44′ E; total area = 110 km2), the third largest city of Greece (approx. 200,000 inhabitants; ELSTAT, 2011). The city is located in SW Greece, delimited by the sea to the north and Mount Panachaikon (1926 m a.s.l.) to the south (Fig. 1). The climate is typical Mediterranean, characterized by a long dry period and a mild winter (average temperatures: annual = 17.9 °C, spring = 20.1 °C) with relatively high precipitation levels (average annual rainfall: 607 mm; http://www.hnms.gr/). Due to inadequate urban planning in the past decades, green spaces within the urban core have been severely shrunk (Papadatou-Giannopoulou, 1991), while the surrounding peri-urban area is covered by agricultural land, orchards (mostly olive groves), Mediterranean shrublands, and a few scattered small remnant patches of coniferous or deciduous forests and riparian vegetation. To standardize sampling effort along the urbanization gradient, we defined three strata of decreasing urbanization. We overlaid a grid of 500 × 500 m cell size and in each cell, we calculated the proportion of built cover using the Urban Atlas land use classification (EEA, 2010). We then stratified grid cells into the three strata based on their proportion of built cover (Clergeau, Croci, Jokimäki, Kaisanlahti-Jokimäki, 80

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& Dinetti, 2006): the “urban zone” represented the densely built city core (> 50% built cover), the “suburban zone” comprised areas of medium density urban fabric (30% - 50% built cover), and the “periurban zone” encompassed the outskirts where built cover was lower than 30%. We defined 45 sampling sites, by randomly selecting 45 grid cells (15 cells within each zone; Fig. 1), on condition that each site was situated at least 500 m from the nearest site to avoid double-counts.

2.4. Data analysis As the urbanization gradient was defined based on a land-use classification system (EEA, 2010), we tested for differences in the percent cover of the land-cover types among the pre-defined urbanization zones at a finer scale using Analysis of Variance (ANOVA). We used species richness (S) and abundance (N) to assess butterfly diversity in each sampling site (Santini et al., 2017), considering the pooled number of species and the summed abundance of each species across the three visits respectively (Slancarova et al., 2016). Butterfly species were then assigned to three feeding categories based on their larval feeding specificity: monophagous (species that feed on one hostplant), oligophagous (species that use different species of the same genus) and polyphagous (species that feed on several species of different genera or families; Eskildsen et al., 2015). Monophagous and oligophagous were treated as one group in the analyses (henceforth referred as oligophagous), since the former group consisted of only three species. Diversity comparisons among the different strata of the urbanization gradient were performed with the Kruskal-Wallis test and pairwise comparisons were carried out with the Mann-Whitney U test (at the α = 0.05 level). As the multicollinearity test conducted among the land-cover types indicated strong negative correlation between woody vegetation and built cover and between the former and impervious surfaces cover (Spearman rank coefficient r = −0.75 and r = −0.80 respectively, p < 0.001), a Principal Components Analysis (PCA) was used to create independent explanatory variables. We carried out generalized linear models (GLMs) using Poisson and negative binomial distribution with log link function to model butterfly species richness and abundance, respectively. The covariates were: (1) the PCA axes (PC1, PC2) explaining most of the variability in land cover types (see Results), (2) plant species richness (PLANT.S), (3) the number of flower heads (FLOWER), (4) the mean height of herbaceous vegetation (HERB.H) and (5) the log-transformed mean larval host-plant cover (LHPC). Model residuals were examined for linearity, overdispersion and homoscedasticity. We first constructed univariate models and proceeded by adding explanatory variables until the global model was built. For the assessment of the confidence set of best models, multimodel inference was used (Burnham & Anderson, 2002). Models were ranked according to their Akaike Information Criterion value (AIC) to identify those with the best fit (i.e. with the lowest AIC value). After performing a correction for small sample size (AICc), model weights were generated based on the difference (ΔAICci) between the AICc value of the best model and the AICc value of each of the selected models. Since models with ΔAICci < 2 are considered as good as the “best” model (Richards, 2005), we employed model-averaging to reduce model selection uncertainty (Burnham & Anderson, 2002). To evaluate the relative importance of each covariate for butterfly species richness and abundance, we summed the Akaike weights across all models that included the covariate under consideration (Burnham & Anderson, 2002). Furthermore, Redundancy Discriminant Analysis (RDA) was employed to investigate the relationship between the land cover and habitat variables used in the GLMs and community composition. Species with less than three occurrences were removed from the analysis, while abundance data were log-transformed to down-weight the influence of extreme values (Leps & Šmilauer, 2003). Permutation tests (with 999 permutations under the reduced model) were used to assess the significance of the relations between species distributions and explanatory variables. Statistical analyses were performed with R 3.3.1 (R Core Team, 2016) using the packages “MASS” (Venables & Ripley, 2002) and “vegan” (Oksanen et al., 2013).

2.2. Butterfly surveys Butterflies were recorded along fixed transects of 300 m length, walking at a constant pace, within 5 m to the side (2.5 m each side), above and in front of the observer (Pollard, 1977), and lasting 30 min on average. Each of the 45 sites was sampled three times between April and June 2015 at approximately monthly intervals. To reduce detectability error, butterflies were also counted during the return route and thus, relative abundance of each species along each transect was calculated as the maximum abundance of the two routes (GonzálezEstébanez, García-Tejero, Mateo-Tomás, & Olea, 2011). All surveys were conducted by the same observer in sunny conditions and without strong wind. Individuals were captured with a hand aerial net and released after identification. Identification was based on Lafranchis (2014) and nomenclature followed van Swaay et al. (2010). Transects were positioned across the most representative habitats within each grid cell and as close to vegetation as possible to maximize the probability of detecting butterflies (Clark et al., 2007). In residential areas transects were located along the boundaries of the properties, so that both the residential gardens and the road verges and pavements were surveyed (Baldock et al., 2015). 2.3. Land cover and local habitat characteristics Five predominant land-cover types shaping the studied urban landscape were defined: (1) buildings, (2) impervious surfaces (roads, parking spaces and other artificial areas), (3) woody vegetation, (4) open green spaces (open spaces with lawns and herbs) and (5) water bodies. We delineated polygons of the different land-cover types within a 200-m radius circular buffer zone around each transect, centred at the midpoint of the transect, using Google Earth Imagery (2015) as a base layer, and then we calculated the percent cover of each land-cover type in each buffer using ArcGIS 10.1 (ESRI). Additionally, we conducted vegetation surveys to describe the local habitat of butterflies during the highest flower density period (April – May 2017). We established three 5 × 2 m vegetation plots along each transect route (135 plots in total), always situated in green spaces with high cover of herbaceous vegetation at the start-, mid- and endpoint of each butterfly transect (Curtis, Brereton, Dennis, Carbone, & Isaac, 2015). Where this was not possible due to the presence of artificial structures, a minimum distance of 100 m between plots was maintained. Within each plot we recorded the cover of each plant species and then calculated the mean species cover for each site. Additionally, to assess the available plant resources for butterflies at the site level, we calculated: (a) the total plant species richness (maximum number of species from the three plots), (b) the mean height (cm) of herbaceous plants from the three plots, (c) the total abundance of flower heads (sum of the three plots), and (d) the mean cover (%) of all potential larval host-plants (LHP) from the three plots. LHP cover at the plot level was estimated as the cumulative cover of the potential host-plant species for all butterfly species of the study area (Tolman & Lewington, 2008; T. Lafranchis, personal communication, August 3, 2017). We assumed that plant species cover and composition remained relatively stable during the whole study period, as was indicated by a pilot vegetation survey we conducted in 2015. 81

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p < 0.001; N: U = 185.5, p = 0.002) and the suburban zone (S: U = 183, p = 0.003; N: U = 160.5, p = 0.048). Even though butterfly diversity was higher in the suburban zone compared to the urban, no significant differences were observed (p > 0.05). Similarly, both polyphagous and oligophagous species richness increased significantly in the peri-urban zone. Nine unique species (i.e. not found in other zones) were recorded in the peri-urban zone (Appendix A).

3. Results A total of 1805 individuals belonging to 41 butterfly species were recorded in the study area (Appendix A), with a mean of 40.11 individuals (range = 2–122, standard error SE = 3.62) and 9.5 species (range = 2–20, SE = 0.69) per site. The most abundant species were Pieris rapae (43% of the total abundance) and Maniola jurtina (11% of the total abundance) recorded in 100% and 78% of the sampling sites, respectively, while 15 species occurred in less than 10% of the sampling sites (Appendix A). The butterfly community also included Zerynthia polyxena, a species listed in the European Habitats Directive 92/43 (Annex IV). The number of oligophagous species was significantly lower than that of polyphagous ones (polyphagous: 23, oligophagous: 18; t = −9.045, p ≪ 0.001; Appendix A). Oligophagous species also had significantly lower abundances (mean = 4.7, SE = 0.83) than polyphagous species (mean = 35.3, SE = 3.15; t = −9.44, p ≪ 0.001). We recorded 300 plant species (belonging to 204 genera and 67 families), comprising 37% of the total wild vascular flora recorded in Patras (Chronopoulos & Christodoulakis, 1996). Of the taxa recorded, 41.5% belong to the families Asteraceae, Fabaceae and Poaceae, which are among the best-adapted families to the ecological conditions of the Mediterranean area. The high percentage of Mediterranean taxa (59.2%) in conjunction with the high percentage of therophytes (51.2%) reflects the Mediterranean character of the study area. The presence of alien plant species in the study area was low (4%).

3.3. Drivers of butterfly diversity patterns PCA showed that the first two principal components accounted for 73.6% of the total variance in the land-cover variables (Table 2). The first principal-component axis (PC1) depicted a gradient from high building and impervious surfaces density (positive values) to high woody vegetation cover (negative values), whereas in the second principal axis (PC2) water bodies had positive values and open green spaces negative. The confidence set of best models that resulted from multimodel inference is presented in Table 3. Overall butterfly species richness and abundance was negatively affected by increasing built cover and positively associated with increasing woody vegetation cover (cumulative weight of PC1 = 1.00, Table 4). All other covariates had an almost negligible influence on either species richness or abundance. A similar pattern was observed for polyphagous and oligophagous species richness, which was also negatively associated with built cover (Table 4). RDA indicated that environmental variables explained 24.8% of the total variance of the butterfly community. Monte Carlo permutation tests demonstrated that the gradient from high building and impervious surfaces cover to woody vegetation cover (PC1) had the greatest influence on butterfly community structure (pseudo-F = 6.602, p = 0.001; Fig. 3).

3.1. Vegetation and habitat features along the urbanization gradient Vegetation cover was lowest in the urban zone (33%), intermediate in the suburban zone (60%) and highest in the peri-urban zone (90%). Accordingly, built cover was found to be significantly higher in the urban zone (64%) than in the other two zones (Table 1). The mean number of plant species was 20.03 (SE = 0.57) at the plot and 41.29 ( ± 1.84) at the site level (Table 1). Plant species richness was significantly higher in the peri-urban zone than in the urban (U = 202.5, p < 0.001) and the suburban zone (U = 170.5, p = 0.017), whereas no significant differences were found between the urban and the suburban zone (p > 0.05). Mean height of herbaceous vegetation was lower in the peri-urban zone (U = 47.5, p = 0.007). The number of flower heads and the larval host-plant cover did not show any differences among the zones (Table 1).

4. Discussion The city of Patras has undergone a rapid, haphazard densification and expansion during the last century due to industrial development and population influx. The inefficient planning strategies were unable to control urban sprawl and gradually led to a remarkable shrinkage and fragmentation of green spaces in the urban zone (PapadatouGiannopoulou, 1991). Furthermore, natural ecosystems in the surroundings of the city have been used for agricultural purposes and nowadays are principally converted to olive groves. These social and economic factors have shaped a densely built urban area surrounded mainly by agricultural mosaics with great woody vegetation cover represented mainly by olive trees and Mediterranean shrubs (Fig. 1). However, despite the intensive landscape and habitat modifications, the urban environment supports a very diverse plant community with a

3.2. Butterfly diversity along the urbanization gradient Butterfly diversity decreased along the urbanization gradient (Fig. 2), as both species richness (S) and abundance (N) were significantly higher in the peri-urban zone than in the urban (S: U = 200,

Table 1 Land cover and local habitat variables measured along the urbanization gradient: average cover (%) of land-cover types per urbanization zone ( ± SE), their statistical comparisons performed with ANOVA, mean ( ± SE) plant species richness, number of flower heads, height of herbaceous vegetation and larval host-plant cover (%) per urbanization zone, their comparisons performed with Kruskal-Wallis test, and total values for the whole study area (*: p < 0.05, **: p < 0.01, ***: p < 0.001; superscript letters indicate significant differences based on pairwise comparisons). Variable

Urban zone

Suburban zone

Peri-urban zone

Comparison

Total

Land cover variables Buildings Impervious surfaces Open green spaces Woody vegetation Water bodies Sea

37.13 ( ± 4.36)a 26.86 ( ± 2.12)a 11.60 ( ± 1.91)a 21.86 ( ± 2.44)a 0.14 ( ± 0.17) 2.42 ( ± 2.42)

15.06 ( ± 1.97)b 18.71 ( ± 1.95)b 27.30 ( ± 2.51)b 32.95 ( ± 3.04)b 3.12 ( ± 2.50) 2.89 ( ± 2.09)

2.86 ( ± 0.85)c 5.08 ( ± 0.39)c 29.41 ( ± 4.17)b 60.49 ( ± 4.75)c 2.16 ( ± 1.17) 0.00

F 38.28*** 37.60*** 10.37*** 31.39*** 0.90 0.70

18.35 16.88 22.77 38.43 1.85 1.76

Local habitat variables Plant species richness Number of flower heads Herbaceous vegetation height (cm) Larval host-plant cover (%)

33.13 ( ± 2.51)a 714.00 ( ± 128.94) 46.44 ( ± 4.98)ab 35.32 ( ± 3.27)

39.73 ( ± 2.26)a 678.00 ( ± 86.06) 51.56 ( ± 4.01)a 38.63 ( ± 4.68)

51.00 ( ± 2.95)b 775.00 ( ± 119.05) 37.67 ( ± 2.83)b 42.23 ( ± 4.79)

χ2 15.42*** 0.18 7.32* 0.39

41.29 ( ± 1.84) 722.27( ± 63.94) 45.22 ( ± 2.43) 38.73 ( ± 2.46)

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Fig. 2. Mean ( ± confidence interval) butterfly abundance (a) and species richness (b) along the urbanization gradient. Table 2 Results of PCA performed for the land cover variables used to assess the habitat for the butterfly community. Only components with eigenvalues > 1 are shown here.

Eigenvalue Variance explained (%) Scores Buildings Open green spaces Impervious surfaces Water bodies Woody vegetation

PC 1

PC 2

2.519 50.388

1.162 23.244

0.556 −0.338 0.530 −0.085 −0.539

−0.047 −0.597 −0.012 0.777 0.192

Table 4 Model-averaged coefficients ( ± SE) for all variables that were included in the set of best-ranked models (i.e. those with ΔAICc < 2) and cumulative model weights (i.e. summed Akaike weights), indicating the relative importance of each variable (S: butterfly species richness; N: butterfly abundance; Oligophagous – S: species richness of oligophagous butterflies; Polyphagous – S: species richness of polyphagous butterflies).

clear Mediterranean character (see also Chronopoulos & Christodoulakis, 1996). The representation of alien plant species is low because of the resilience of the highly competitive native flora to the intense and long-standing human interference (Pignatti, 1983; Werner, 2011). The number of butterfly species is relatively large (including a species of conservation interest), similar to that of other Mediterranean cities (Bergerot et al., 2010; Lizée, Tatoni, & Deschamps-Cottin, 2016) and as expected, higher compared to European urban areas of higher latitudes (e.g. Öckinger et al., 2009).

Metric

Variable

Coefficient

Cumulative weight

S

PC1 FLOWER HERB.H PC2 LHPC

−0.225 (0.033) 0.000 (0.000) −0.003 (0.001) 0.032 (0.006) −0.055 (0.014)

1.000 0.196 0.167 0.152 0.129

N

PC1 HERB.H FLOWER LHPC

−0.207 (0.051) 0.005 (0.001) 0.000 (0.000) −0.403 (0.092)

1.000 0.200 0.191 0.184

Oligophagous – S

PC1 HERB.H LHPC

−0.211 (0.070) −0.007 (0.002) −0.470 (0.135)

1.000 0.277 0.214

Polyphagous – S

PC1 FLOWER PC2

−0.229 (0.038) 0.000 (0.000) 0.028 (0.009)

1.000 0.282 0.196

Table 3 Best-ranked GLMs for butterfly species richness (S), abundance (N) and species richness of each larval host-plant specialization group (oligophagous vs. polyphagous species), showing the Akaike Information Criterion (AIC), the number of parameters (k), the AIC corrected for small-sample size (AICc), the differences in AICc (ΔAICc = AICci − AICcbest) and model’s Akaike weight (wi). Only models with ΔAICc < 2 were considered in model averaging. Metric

Rank

Model

AIC

k

AICc

ΔAICc

wi

S

1 2 3 4 5

PC1 PC1 + FLOWER PC1 + HERB.H PC1 + PC2 PC1 + LHPC

234.25 235.14 235.46 235.65 235.98

2 2 2 2 2

234.53 235.43 235.75 235.94 236.27

0.00 0.89 1.21 1.40 1.73

0.356 0.196 0.167 0.152 0.129

N

1 2 3 4

PC1 PC1 + HERB.H PC1 + FLOWER PC1 + LHPC

371.17 398.85 398.94 399.02

2 2 2 2

371.46 399.14 399.23 399.31

0.00 1.21 1.30 1.38

0.425 0.200 0.191 0.184

Oligophagous – S

1 2 3

PC1 PC1 + HERB.H PC1 + LHPC

151.92 152.84 153.35

2 3 3

151.92 153.43 153.94

0.00 1.22 1.73

0.509 0.277 0.214

Polyphagous – S

1 2 3

PC1 PC1 + FLOWER PC1 + PC2

213.02 213.95 214.68

2 3 3

213.02 214.54 215.27

0.00 1.23 1.96

0.522 0.282 0.196

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Fig. 3. RDA plot indicating the influence of each environmental variable on butterfly community structure.

resources for adults (number of flower heads) and larvae (host-plant cover) had a trivial influence on the butterfly community, compared to the impact of built cover. It seems that green patches in the urbanized areas are probably too small, scarce and isolated. Habitat fragments, too small and isolated or of too low quality, are generally unable to support large numbers of species or viable populations (Leibold et al., 2004; Snep et al., 2006; Soga et al., 2015; Wood & Pullin, 2002). Thus, their colonization largely depends on immigration events from adjacent source areas (Leibold et al., 2004). Even if plant resources were available within the urban zone, we wouldn’t expect higher butterfly diversity, since many specialists might not be able to reach and exploit these plant resources due to high habitat fragmentation and isolation (Bergerot et al., 2010). Oligophagous species are, as expected, relatively rare in the study area. McKinney (2008) showed that different species may present varying responses to urbanization, and butterfly species with larval feeding specialization are considered less likely to occupy the urban areas (Clark et al., 2007). In addition, polyphagous species are less common in the more urbanized areas. Overall, the Patras butterfly community showed a negative response to increasing urbanization. The only exception to the negative effect of urbanization were some generalist and/or opportunistic species, such as the Lang’s Short-tailed Blue (Leptotes pirithous) and the Small White (Pieris rapae), also reported in other urban areas by Lizée, Mauffrey, Tatoni, and Deschamps-Cottin (2011). The Small White is a generalist and tolerant species and, as an excellent colonizer even of disturbed habitats, it occurs in a large variety of habitats and elevational zones (Kocher & Williams, 2000). In addition, the Geranium Bronze (Cacyreus marshalli) and the Mallow Skipper (Carcharodus alceae) are more abundant in the urban zone, although they are food plant specialists. The Geranium Bronze is a nonnative species showing a remarkable range expansion in Europe (Numa et al., 2016), associated with man-made green spaces (Bergerot et al., 2010), as it depends on cultivated Pelargonium plants, which are one of the most popular plants cultivated even in balconies of blocks of flats. The Mallow Skipper feeds on mallows (Malva spp.), which are also very widespread in the study area found in any abandoned land. Studying the landscape structure, such as green patch size, shape and connectivity, would provide more insight into the drivers of butterfly diversity patterns. Despite the increasing urban and suburban

Our results demonstrate the great ecological importance of the periurban area that exhibits higher butterfly species richness and abundance than the other two more densely built zones. Possibly the higher butterfly diversity in the peri-urban zone could be attributed to the presence of larger and less fragmented green patches (Beninde et al., 2015) of a wide variety of suitable habitats (Snep et al., 2006), and to the higher cover of natural and semi-natural vegetation (Chong et al., 2014). In contrast, the significant decrease in butterfly species richness and abundance in the urban zone denotes the impoverishment of the butterfly community possibly due to the considerable shrinking of vegetation and suitable habitats. However, despite the respective significant increase of vegetation cover from the urban to the suburban zone, butterfly diversity was not significantly higher in the latter. This contradicts the findings of a recent ornithological study in the same area that showed a clear and significant gradual increase of urban bird diversity along the urbanization gradient (Tzortzakaki, Kati, Kassara, Tietze, & Giokas, 2017) and illustrates the idiosyncrasies of each group of organisms. Therefore, it seems that, for butterflies, vegetation cover alone is not a sufficient factor to enhance species richness and abundance, but only in conjunction with the extent and connectivity of vegetated areas. Green spaces in the suburban area are probably unsuitable for butterflies, as they constitute mostly scattered small patches of vegetation such as gardens, unmanaged lots and road verges. Our findings highlight the negative impact of built cover on the local urban butterfly community and a respective positive effect of vegetation cover. The detrimental effect of built-up areas documented by our models is also supported by other studies (Clark et al., 2007; Ramírez Restrepo & Halffter, 2013; Ruszczyk & Mellender de Araujo, 1992), as urban features such as roads and buildings are linked with decreased butterfly diversity (Chong et al., 2014; Clark et al., 2007). Man-made structures might act as barriers for butterfly dispersal (Bergerot et al., 2010; Lizée et al., 2016). Butterflies may be mobile organisms, but remote disconnected urban green patches among built surfaces may act as “sinks”, trapping butterflies which then cannot disperse to other habitats of suitable extent and characteristics (Snep et al., 2006). Although the positive effect of plant resources on butterfly diversity has been emphasized (Lizée et al., 2016; Leston & Koper, 2017; Matteson & Langellotto, 2010), contrary to our expectations, the food 84

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sprawl in Patras city, the occurrence of specialist species and species of conservation interest supports the conservation value of green spaces and highlights the importance of designing appropriate conservation actions. We underline the need that municipal and planning authorities produce explicit and up-to-date city plans, as a prerequisite for adequate green space management aiming to reverse their long-term degradation. A robust designation in land use clearly demarcating green spaces (of sufficient extent and adequate vegetation structure) and buffer zones could contribute to a more sustainable long-term urban development mitigating biodiversity loss. We anticipate that in a densely built city, ensuring the connectivity of green spaces (Öckinger et al., 2009) and even better the existence of green corridors from the peri-urban to the urban zone (Beninde et al., 2015) could facilitate butterfly dispersal towards more urbanized areas (Snep et al., 2006) by enhancing the vegetation of underused open spaces (Leston & Koper, 2017). Furthermore, even if our results did not reveal any strong relationships between habitat quality and butterfly diversity, we consider that the quality of green spaces is a key factor for butterfly survival (Thomas et al., 2001) and thus, it should also be taken into consideration. Suitable natural or semi-natural habitats may be vital for butterflies (Chong et al., 2014; Wood & Pullin, 2002), providing resources for both larval and adult stages (Snep et al., 2006). Perhaps the adoption of more traditional low-intensity farming in a broader area could gradually shape landscape and habitat features with the potential to support a more balanced butterfly community.

5. Conclusions Despite the degradation of the urban environment of Patras, overall the local butterfly community supported a relatively large number of species, including several specialists and a species of conservation interest. Butterfly species richness and abundance were significantly higher in the outskirts (peri-urban zone), where larger and less fragmented green areas are present, while the densely-built urban core and the suburban areas probably did not provide suitable habitats. The surrounding land cover had a prominent influence on butterfly species richness and abundance, as a negative impact of built-up areas and a concomitant positive role of vegetation were found. Contrary to findings of other studies, plant resource availability did not influence significantly butterfly diversity and community structure. Further research on landscape structure including green space connectivity could provide more insight into the drivers of the impoverishment of the urban butterfly community. Finally, it is important to draw the attention of local authorities and urban planners to undertake conservation actions towards maintaining and enhancing butterfly diversity. Acknowledgements We are grateful to Tristan Lafranchis for kindly providing the larval host-plant list for the study area. We thank George Iliopoulos for help and support during all stages of the study, Cristina Kassara for statistical advice, and Dimitris Papandropoulos for assistance in field work.

Appendices Appendix A. Butterfly species recorded in the study area, total number of individuals (N), number of occurrences (sites with species presences) and larval host-plant specialization (O: monophagous & oligophagous, P: polyphagous species). Species indicated with asterisk (*) were present only in the peri-urban zone. Family

Species

N

Occurrences

Host-plant specialization

Papilionidae Papilionidae Papilionidae Pieridae Pieridae Pieridae Pieridae Pieridae Pieridae Pieridae Pieridae Pieridae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Lycaenidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Nymphalidae Hesperiidae

Papilio machaon Iphiclides podalirius Zerynthia polyxena Pieris brassicae Pieris rapae Pontia edusa Euchloe ausonia Anthocharis cardamines Colias crocea Gonepteryx rhamni* Gonepteryx cleopatra Leptidea sinapis Satyrium ilicis* Callophrys rubi* Lycaena phlaeas Lampides boeticus Cacyreus marshalli Leptotes pirithous Celastrina argiolus Glaucopsyche alexis* Pseudophilotes vicrama* Aricia agestis Polyommatus thersites Polyommatus icarus Charaxes jasius* Limenitis reducta Vanessa atalanta Vanessa cardui Melitea didyma Brintesia circe* Maniola jurtina Pyronia cecilia* Coenonympha pamphilus Pararge aegeria Lasiommata megera Kirinia roxelana* Carcharodus alceae

75 35 3 56 781 10 31 11 97 9 59 9 3 4 6 5 16 8 9 8 1 11 4 69 3 3 7 24 2 1 204 19 23 95 15 1 28

23 19 2 24 45 9 15 9 30 2 18 5 2 3 5 4 6 4 7 6 1 9 4 26 2 3 6 14 2 1 35 3 13 27 9 1 13

P O O P P P P P P O O P O P O P O P P P O P O P O O O P P O P O P P P O O

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O. Tzortzakaki et al. Hesperiidae Hesperiidae Hesperiidae Hesperiidae

Thymelicus acteon Thymelicus sylvestris Ochlodes sylvanus Gegenes pumilio

12 32 15 1

6 7 6 1

Total

1805

45

O P P O

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