Sensitivity of insectivorous bats to urbanization: Implications for suburban conservation planning

Biological Conservation 146 (2012) 41–52

Contents lists available at SciVerse ScienceDirect

Biological Conservation journal homepage: www.elsevier.com/locate/biocon

Sensitivity of insectivorous bats to urbanization: Implications for suburban conservation planning Caragh G. Threlfall a,⇑, Bradley Law b, Peter B. Banks a,c a

Evolution and Ecology Research Centre, School of Biological Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia Forest Science Centre, NSW Primary Industries, Beecroft, NSW 2119, Australia c School of Biological Sciences, University of Sydney, NSW 2006, Australia b

a r t i c l e

i n f o

Article history: Received 1 June 2011 Received in revised form 23 November 2011 Accepted 26 November 2011 Available online 20 December 2011 Keywords: Insectivorous bat Species–habitat models Urban ecology Community ecology

a b s t r a c t Effective conservation planning requires an understanding of species–habitat relationships across a diverse array of taxa, yet many studies typically focus on conspicuous fauna. Using systematic acoustic surveys, we examine the response of insectivorous bat species to urbanization and quantify species–habitat relationships to classify species in terms of their tolerance or sensitivity. Surveys were conducted in Sydney, Australia, during spring–summer of 2008 in 29 defined 25 km2 landscapes, across various land uses. We quantified bat–habitat relationships using local and landscape scale variables. We recorded 17 taxa across the urban gradient, with substantial variation in the tolerance and sensitivity of each species. The density of houses, landscape geology, the amount of bushland (ha) exclusively on fertile geologies and moth biomass were the most frequent predictors of individual activity, explaining more than 60% of variation in activity for some species. Importantly, the area of bushland on poorer soils was not a good predictor, highlighting the need for caution when interpreting results of large scale studies which do not account for variations in habitat productivity. Species-specific differences existed, although the majority of the assemblage was considered to be urban-sensitive. Many of these sensitive species were most active in fertile suburban habitats, with an average of 12–28% bushland cover within 5 km. Our study demonstrates the necessity to elucidate species-specific habitat relationships, and suggests bats would benefit from the conservation of productive suburban bushland remnants and riparian habitats, while improving connectivity to these areas via the maintenance of tree cover across the matrix. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Urbanization is a complex process of landscape change resulting in altered fauna assemblages (Grimm et al., 2008). Such changes to the landscape facilitate the commonness of some species, hinder movements of others, and alter resource availability for most (McKinney, 2002; Shochat et al., 2006). Local and landscape scale variables that influence species response to such habitat modification include: forest cover in the landscape (Mortelliti et al., 2010); the nature of the matrix (Ricketts, 2001); the extent of fragmentation and habitat loss (Fahrig, 1997); and structural complexity of the remaining habitat (Holland and Bennett, 2007). In addition to varied responses to landscape and habitat components, species responses can also vary between geographic regions (Rhodes et al., 2008). As such, generalizations about the nature of ⇑ Corresponding author. Present address: Department of Resource Management and Geography, Burnley campus, The University of Melbourne, 500 Yarra Boulevard, Richmond, Victoria 3121, Australia. Tel.: +61 3 9250 6892. E-mail addresses: [email protected] (C.G. Threlfall), bradl@sf. nsw.gov.au (B. Law), [email protected] (P.B. Banks). 0006-3207/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2011.11.026

fauna responses to disturbances such as urbanization should be made cautiously, as different taxa can have very different responses to the same process (Garden et al., 2010). The impacts of urbanization will depend on the ecological characteristics of individual species; hence, information on species-specific responses is critical. Previous work suggests that some species are inherently pre-adapted or more able to adjust to urban environments, however many non-tolerant species are not (Marzluff, 2001). Hence, there is an urgent need to identify sensitive and tolerant species and their habitat requirements, particularly in the face of an expanding human population (United Nations Department of Economic and Social Affairs, 2010). By employing an urban–rural gradient approach (sensu McDonnell and Pickett, 1990), species can be grouped to identify their sensitivity to urbanization. Several studies have classified taxa into categories reflecting their urban tolerance, including ‘urban adapters’, ‘urban avoiders’ and ‘urban exploiters’ (Blair, 1996; Blair and Launer, 1997; Germaine and Wakeling, 2001; McKinney, 2002). Adapters can adapt to urban habitats but also use natural resources; avoiders are extremely sensitive and disappear quickly from urban landscapes; and exploiters are almost completely

42

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

dependent on resources provided by humans (Blair, 1996; McKinney, 2002). However, studies of this nature are currently dominated by examination of bird responses, whereas comparatively few studies examine other vertebrate fauna (McDonnell and Hahs, 2008; McKinney, 2008). Insectivorous bats are capable of flight and more mobile than other vertebrate groups. This mobility can allow some species to move between habitat patches, allowing them to be more tolerant of habitat fragmentation (Ethier and Fahrig, 2011; Law et al., 1999), although not all species tolerate fragmentation, especially certain species inhabiting tropical rainforests (Meyer et al., 2008; Struebig et al., 2008). The effects of fragmentation are likely to be context-specific, with urban landscapes considered generally harsher environments than agricultural or forested areas. To certain species, much of the human-altered matrix (residential and industrial areas, etc.) may be more inhospitable than an agricultural matrix of old, scattered trees (Basham et al., 2011). Of the urban bat studies conducted to date, some show decreased species diversity or activity in highly urbanized areas (Gaisler et al., 1998; Jung and Kalko, 2011; Kurta and Teramino, 1992), while other studies show higher overall species activity or richness in urban parks (Avila-Flores and Fenton, 2005) and suburban areas (Hourigan et al., 2010; Threlfall et al., 2011). Measures of overall bat activity in urban landscapes suggest that certain bats may be positively related to woodland cover (Gehrt and Chelsvig, 2003; Walsh and Harris, 1996) or bushland cover (Basham et al., 2011; Threlfall et al., 2011). However, as habitat preference, roost flexibility, flight mobility and hence foraging guilds differ greatly among species (Aldridge and Rautenbach, 1987; Kunz and Lumsden, 2003), different species will likely show dramatic differences in their use and tolerance of urban landscapes, where some can roost in artificial structures (e.g. Evelyn et al., 2004) and forage around street lights (Avila-Flores and Fenton, 2005; Geggie and Fenton, 1985), whereas others cannot (Jung and Kalko, 2011). Few studies to date have examined speciesspecific habitat associations in urban landscapes to identify which species are more tolerant or vulnerable (Basham et al., 2011; Gehrt and Chelsvig, 2004; Jung and Kalko, 2011), and there is limited information about the changing composition of bat assemblages in many urban landscapes worldwide. In the present study, we investigate the use of the urban landscape by individual bat species in the rapidly expanding city of Sydney, Australia, and build upon a number of studies in that city. A previous study in a smaller, well-vegetated part of Sydney (Basham et al., 2011) suggested that activity and occurrence of most bat species was a function of the broad spatial context, vegetation cover, and various microhabitat variables. Subsequently, a Sydney wide study identified that total bat activity and species richness was lower in the most urbanized parts of the matrix and found that the response of bats could be allocated to functional groups based on wing morphology and echolocation traits (Threlfall et al., 2011). While these studies elucidate important processes influencing functional groups of bats and overall activity in urban areas, successful conservation requires better knowledge of the response of individual species, including models of habitat requirements. This study aimed to classify individual bat species as sensitive or tolerant to urbanization. We hypothesize those environmental variables that characterize resource and habitat gradients at both a landscape and patch scale influence the composition of the bat assemblage as a whole, and influence the activity of each species in the assemblage in different ways. We predict that urbanization alters the composition of the bat assemblage along the urban gradient, and that the majority of the assemblage will be sensitive to urbanization. We also predict that the sensitivity or tolerance of each species is related to species traits and habitat use.

2. Method 2.1. Study area Study sites were located within a 60 km radius of the Central Business District (CBD) of Sydney, Australia, covering a 4000 km2 area. Sydney is Australia’s oldest and largest city, supporting almost four million people. Sydney’s vegetation communities are distributed across two primary geologies of the area, the Wianamatta shale, including some of the Narrabeen group shales and Hawkesbury sandstone complex (Benson and Howell, 1995). Historically, the low lying shale plain was cleared for agriculture due to its fertile soil, and it is now largely covered by urban and suburban development (Kartzoff, 1969). Much of the nutrient poor sandstone plateaux were unsuitable for farming and settlement, and these areas contain leafy suburbs dissected by vegetation which forms part of Sydney’s National Park system, in addition to larger National Parks on surrounding steep areas on poorer soil (Benson and Howell, 1995; Kartzoff, 1969). This type of non-random development is typical of cities, as factors influencing patterns of human settlement are similar worldwide (Haberl et al., 2004). As such, agricultural areas in many cities are now being converted to suburbs and remnant vegetation remains in areas previously ‘unwanted’ for development or commercial purposes (Pressey, 1994). 2.2. Study design Bat activity was sampled using the same study design as Threlfall et al. (2011), in 29 randomly selected replicate 5  5 km ‘landscapes’. Landscapes were categorized based on the average level of urbanization and vegetation cover of each landscape, whilst also capturing variations in geology where possible. The categories were: ‘urban’ (>5 dwellings/ha and <10% vegetation cover); ‘suburban’ (2–5 dwellings/ha and 5–40% vegetation cover); and ‘vegetated’ (<5 dwelling/ha and >40% vegetation cover). Suburbs in Sydney were built on both types of geology. As such, suburban landscapes were further classified into: ‘suburban shale’ (>80% of landscape dominated by shale), ‘suburban sandstone’ (>80% of landscape dominated by sandstone), and ‘suburban transition’ (landscape within a predominately >40% shale and >40% sandstone transitional area). Landscapes in the urban category occurred mainly on shale, but also on shale sandstone transition. Landscapes in the vegetated category mainly occurred on sandstone, but included one shale sandstone transition area. Four landscape elements were sampled within each landscape to sample both natural and man-made habitats available to bats: remnant bushland (>2 ha mapped remnant vegetation), riparian areas (natural mapped waterway 2–10 m wide), open space/parkland and residential/built areas (typically backyards). Each landscape element was located >500 m apart, and data were collected at 113 landscape elements, within 29 defined landscapes (six replicate landscape ‘blocks’ of the urban, suburban shale, suburban transition and vegetated category, and five replicate landscape ‘blocks’ of the suburban sandstone category; Fig. 1), where three landscape elements were omitted due to inaccessibility. 2.3. Bat call recording Bat activity was recorded using Anabat detectors (Titley Electronics, Ballina, Australia) onto a CF storage card via a zero-crossing interface (Z-CAIM, Titley Electronics), in the bat maternity season between October and December 2008, as this is when resource requirements are likely to be highest. Each landscape element was sampled remotely for two full consecutive nights from sunset to sunrise (1800–0630 h). The detector microphone was

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

43

Fig. 1. Map of study landscapes in Sydney, NSW, Australia. Study landscapes include Urban (Ur, n = 6); Suburban Shale (SSh, n = 6); Suburban Sandstone (SSa, n = 5); Suburban Transition (STr, n = 6); and, Vegetated (Ve, n = 6) categories. Within each landscape, four elements were sampled: backyard, bushland remnant, riparian corridor and open space, where three elements were omitted due to inaccessibility.

angled up at 45° and set at 1 m high. Detectors were placed on flyways (typical width = 3 m), or facing gaps in vegetation or structural clutter. Sampling was conducted on mild nights, and avoided the two nights either side of the full moon in order to minimize nightly variation. Up to 10 detectors were operating on any one night within multiple site categories. During bat sampling, mean nightly temperature varied between 7 and 23 °C, and averaged 16.0 °C.

cies passed the microphone. Species activity per site is expressed as the number of passes per night, per species, where only one pass per file recorded was used. Average nightly species activity was used instead of total activity recorded over the two nights to account for sites where the detector failed on one night (11% of sites). Data are expressed as means ±1 standard error.

2.4. Automatic bat call identification

We recorded 12 variables describing each element (Supplementary material: Appendix A). At the local scale, we established two 50 m vegetation sampling transects along each flyway sampled, where measurements were taken at five random points (N.B. some data may over-estimate the conditions for the landscape category, for example urban bushland remnants, however these measurements do reflect the microhabitat in which the data was collected). At each point we used the point quadrant method (Krebs, 1989) to asses tree density, hollow bearing tree density, and diameter at breast height (DBH). The distance to the nearest tree in each quadrant was measured and then summed to calculate tree density per hectare. Each of the four trees measured at each point was assessed for the presence of any hollows, where any sized hollow was included in the count (i.e. P2 cm diameter). The proportion of trees containing hollows was then multiplied by tree density to estimate density of hollow bearing trees per site. DBH of each tree was measured by DBH measuring tape. The vertical gap (m) between the top of the understorey and the bottom of the canopy was estimated at each point in order to quantify the vertical space available for bat movement. Vegetation structure was recorded as foliage

A bat pass as defined here comprised a pass with three or more pulses. Bat passes were processed by Anascheme software (Adams et al., 2010; Threlfall et al., 2011), where recorded passes are stored as a single file. An identification key for the Sydney region was used to identify the bat passes to species (Adams et al., 2010). Passes from sympatric Nyctophilus species (Nyctophilus goudli and Nyctophilus geoffroyi), cannot be distinguished and so were combined into a species complex Nyctophilus spp. Species identifications were only made when at least 50% of pulses within a pass were identified to the same species. Species that were considered problematic to identify were double-checked via manual inspections in AnalookW (C. Corben, unpubl., accessed via http://users.lmi.net/corben/anabat.htm). These included Scoteanax rueppellii, Scotorepens orion, Miniopterus schreibersii oceanensis, Nyctophilus spp., Myotis macropus and Vespadelus darlingtoni (Reinhold et al., 2001). Bat passes collected in this way do not measure the abundance of individuals, instead data collected is expressed as bat ‘activity’, which represents the number of times a bat of that spe-

2.5. Site characteristics and landscape variables

44

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

cover multiplied by height in each vegetation layer and averaged per element. Foliage cover was visually estimated for the ground strata, understorey and canopy at each point and was categorized as 1 (<10% cover), 2 (10–29%), 3 (30–49%), 4 (50–69%) and 5 (>70%). Landscape variables were calculated in Arc Map (ESRI, Redlands, California, USA, version 9.3) using GIS layers obtained from the New South Wales (NSW) Office of Environment and Heritage (OEH), NSW Department of Primary Industries (NSW DPI) and the Australian Bureau of Statistics (ABS) (Supplementary material: Appendix A). The distance (km) to the nearest native bushland (>0.5 ha) and mapped watercourse were measured using 1:100 000 scale GIS mapping of drainage and vegetation extent. Landscape heterogeneity (number of land cover types), the amount of native bushland (ha) and housing density (houses/ha) within 5 km of each element was calculated (Threlfall et al., 2011). The variable describing bushland within 5 km is dominated by sandstone geology, as most shale areas are largely cleared. However, we wanted to measure the influence of bushland in the local area on more fertile geologies. As such, the amount of bushland (ha) which fell on shale based geology within 500 m was also calculated for each element to index local high productivity habitat. The total percent of sandstone and shale based geology were calculated for each landscape, using the 1:250 000 GIS mapping of the Geological Map Sheet for Sydney (NSW DPI). 2.6. Prey biomass Flying nocturnal insects were sampled at each site using blacklight insect traps (Australian Entomological Supplies, Bangalow, Australia) and were immediately stored in 70% ethanol. One trap per site was deployed at ground height for one night. Insect sampling was undertaken within the same 3 month period as bat sampling and under similar weather conditions (mean nightly temperature during insect sampling varied between 10 and 24 °C, and averaged 16.8 °C), although on an alternative night to anabat sampling to minimize disruption to the normal flight patterns of species with low intensity calls (Adams et al., 2005). Coleoptera (beetles) and Lepidoptera (moths) were separated from all other insects. All samples were then sorted into four size classes (head–body length); <5 mm, 5–10 mm, 10–15 mm and >15 mm. These classes were based on the range of body sizes of known prey items (O’Neill and Taylor, 1989). The number of individuals per category was recorded, along with the dry weight (g) of all individuals from that category. Insects were dried at 60 °C to a constant weight (ca. 4 days), recorded to the nearest 0.001 g. Dry weight of a known number of individuals was estimated from subsamples. Regression equations were developed to predict the relationship between number of individuals and the total dry weight per category (r = 0.7–0.95). These regression equations were then used to predict the dry weight of insect samples. 2.7. Statistical analysis Bat community composition in a priori defined landscape categories and elements was assessed using a non-metric multidimensional scaling plot (nMDS) using presence/absence data and a Bray–Curtis similarity matrix. Differences in composition and multivariate dispersion between landscape categories and elements were then tested by PERMANOVA, a permutational multivariate analysis of variance, and PERMDISP, a permutational analysis of multivariate dispersion (Anderson, 2001; Anderson et al., 2008). The latter test is necessary as it is analogous to a multivariate test of homogeneity of variance, and if significant may undermine any differences observed using PERMANOVA. Under the PERMANOVA framework, we tested for differences between the fixed effects of landscape category (n = 5), landscape element (n = 4) and their

interaction. Additionally, a random term was added (‘landscape block’), nested within landscape category, to account for the differences between the replicate landscape blocks. Where differences between the fixed effects occurred, pairwise comparisons were used to assess which categories differed significantly. Sites where no species were recorded were removed (20 of the 113 sites), resulting in 93 sites and an unbalanced design to asses community composition. As such, the analysis was run using Type III sums of squares, as this is the most conservative method available (Anderson et al., 2008). SIMPER analysis (Similarity percentages – species contributions) was used to determine which species were responsible for the main differences between site categories (Clarke, 1993). Analyses were carried out in PRIMER (version 6, Plymouth, UK). A Canonical Correspondence Analysis (CCA) was performed to assess the relationships between individual species activity, local, landscape and prey variables (Supplementary material: Appendix A and B). All species detected at five or more sites were included. Relationships were interpreted via visual inspection of a biplot, where points represent species, and vectors represent environmental variables. The position of a species in relation to an environmental vector indicates the strength of the relationship, with a greater correlation indicated by a position further along a vector when a perpendicular line is drawn between it and the species. Vectors pointing in opposite directions indicate inverse relationships. All variables were added initially, and individual variable importance was assessed via inspection of the total percent variation explained, with and without that variable. Both species activity and environmental variables were log (x + 1) transformed prior to analysis as recommended by Palmer (1993). Five sites were removed from the data set because insect sample degradation prevented sorting all samples to size class. Removal of these sites left 108 sites for analysis, including sites where passes unidentifiable to species were recorded. Analyses were conducted using the ‘vegan’ package (Oksanen et al., 2010) in R (R Development Core Team, 2007, version 2.10.0). Relationships between individual species activity and all predictor variables were then assessed using Classification and Regression Trees (CARTs) (De’ath and Fabricius, 2000). Initial analyses using linear modeling performed poorly and were suggestive of non-linear relationships. Additionally, threshold levels for specific variables were considered useful for recommending management actions. Only species which were present in greater than 20 sites were used in this analysis. Each model was offered the same site and habitat variables as the CCA, however species-specific diet variables were defined a priori, and included for each species (see Supplementary material: Appendix B). The same 108 sites as above were used in the analysis. This method repeatedly partitions data defined by predictor variables, and presents the data as a tree or dendrogram, with binary splits based on data structure. The most parsimonious tree model was refined via a cross validation procedure. Here the tree size and deviance explained by additional branches was assessed via the cost-complexity parameter k. The change in deviance explained with increasing tree size, and increasing k thus determines optimal tree size. A correlation of observed and expected values was used to assess tree performance, in addition to calculating an R2 value. Species activity data were log (x + 1) transformed in order to improve model performance. Analyses were conducted using the ‘tree’ package (Ripley, 2010) in R. To assess spatial autocorrelation we calculated Moran’s I for each species in Arc Map. Bat activity was not significantly auto-correlated for any species we modeled using CARTS (Moran’s I = 0.005 to 0.06, P > 0.05).

3. Results Anabat surveys detected 17 taxa over 226 anabat nights, recording 7767 bat passes (average 34.5 ± 4.2 passes/night). Of

45

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

Fig. 2. Average nightly bat activity (passes per night) recorded for each species in the study region across the five landscape categories (a); and four landscape elements (b). Species abbreviations are as follows: Chalinolobus gouldii (Chgo); Mormopterus sp. 2 (Mosp); Vespadelus vulturnus (Vevu); Tadarida australis (Tada); Miniopterus schreibersii oceanensis (Mish); Nyctophilus spp. (Nyct); C. morio (Chmo); Scotorepens orion (Scor); M. norfolkensis (Mono); Scoteanax rueppellii (Scru); Myotis macropus (Myma); Rhinolophus megaphyllus (Rhme); Falsistrellus tasmaniensis (Fata); V. darlingtoni (Veda); M. australis (Miau); C. dwyeri (Chdw); and Saccolaimus flaviventris (Safl).

these passes, 3997 were identified to species or a species complex (51.5%). Of the 17 taxa identified, eight are listed as ‘threatened’ under state legislation. Sites recorded between 0 and 413 passes per night, with 20 sites recording no taxa, as some passes were

Table 1 Permutational analysis of multivariate dispersions (PERMDISP) and pair-wise comparisons testing differences in multivariate dispersion between bat assemblages in landscape categories and elements, analogous to a univariate test of homogenous variance. Bold values indicate significant differences (a = 0.05).

unidentifiable to species level. The maximum number of taxa at any site was 11 in one night, which was recorded twice in fertile suburban riparian landscapes with 12–25% vegetation cover. Table 2 Permutational multivariate analysis of variance (PERMANOVA) and relevant pairwise comparisons testing differences in species composition between bat assemblages found in landscape categories and elements. Bold values indicate significant differences (a = 0.05). Source

df

Pseudo-F

p

4, 26 3, 49 12, 49 24, 49

2.29 1.97 1.36 1.52

0.017 0.043 0.108 0.020

t 1.17 0.72 1.05 1.07 0.34 2.51 1.37 2.11 1.09 2.86 1.41 1.49 1.07 0.80 2.13 1.07

p 0.30 0.73 0.42 0.37 0.93 0.002 0.17 0.02 0.36 0.002 0.13 0.11 0.37 0.62 0.02 0.37

Source

df

Pseudo-F

p

Landscape Element

4, 88 3, 89

4.77 4.76

0.002 0.008

Landscape Element Landscape  element Block (landscape)

t 2.78 0.57 2.70 0.12 2.31 0.11 3.45 2.28 0.79 3.32 1.31 1.20 2.06 0.03 3.54 3.32

p 0.02 0.59 0.01 0.91 0.05 0.91 0.004 0.03 0.48 0.003 0.21 0.24 0.04 0.98 0.002 0.005

Groups Suburban sandstone vs. suburban shale Suburban sandstone vs. suburban transition Suburban sandstone vs. urban Suburban sandstone vs. vegetated Suburban shale vs. suburban transition Suburban shale vs. urban Suburban shale vs. vegetated Suburban transition vs. urban Suburban transition vs. vegetated Urban vs. vegetated Backyard vs. bushland Backyard vs. riparian Backyard vs. open space Bushland vs. riparian Bushland vs. open space Riparian vs. open space

Groups Suburban sandstone vs. suburban shale Suburban sandstone vs. suburban transition Suburban sandstone vs. urban Suburban sandstone vs. vegetated Suburban shale vs. suburban transition Suburban shale vs. urban Suburban shale vs. vegetated Suburban transition vs. urban Suburban transition vs. vegetated Urban vs. vegetated Backyard vs. bushland Backyard vs. riparian Backyard vs. open space Bushland vs. riparian Bushland vs. open space Riparian vs. open space

46

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

Fig. 3. nMDS plot of species composition within (a) landscape categories (O Urban; Suburban shale; D Suburban transition;  Suburban sandstone; + Vegetated); and (b) landscape elements ( Backyard; Bushland; O Open Space; D Riparian). Points closer together are more similar in multidimensional space. Stress = 0.17 for both. (a) shows that the composition of the urban assemblage is different to the composition of suburban shale, (b) shows the composition of the open space assemblage is different to the composition of bushland, as suggested by the PERMANOVA.

Fig. 4. Canonical Correspondence Analysis (CCA) ordination for activity of 15 bat species across environmental and prey variables. Species are in gray, and vectors represent environmental variables. The position of a species in relation to an environmental vector indicates the strength of the relationship, with a position further along a vector indicating a greater correlation. Species abbreviations follow Fig. 1. Variable abbreviations follow Appendix A and B.

Gould’s wattled bat (Chalinolobus gouldii) had the greatest activity across all sites sampled (Fig. 2a and b), and was present in 75% of urban sites. Average activity for this species was 7.2 passes per night and it was detected almost three times as often as the next most recorded bat species, the eastern freetail bat (Mormopterus sp. 2, Adams et al., 1988), at an average of 2.8 passes per night. Seven species were present in more than 20% of sites, listed as the first seven species in Fig. 2a and b. Of these species all were present in all landscape elements sampled, however some were noticeably absent from the ‘urban’ category; including the little forest bat (Vespadelus vulturnus) and the chocolate wattled bat (Chalinolobus morio).

3.1. Species composition along the urban gradient The PERMDISP analysis revealed that assemblages between landscape categories and elements differed in dispersion (Table 1). PERMANOVA analysis revealed that landscape categories and elements comprised significantly different bat assemblages (Table 2). Pairwise comparison revealed that the assemblage in urban landscapes were significantly different to suburban shale landscapes, suburban transition landscapes and vegetated landscapes. However the latter two comparisons also differed in multivariate dispersion (as seen via the PERMDISP), hence we cannot conclusively state whether the difference is due to assemblage

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

structure, or dispersion (Tables 1 and 2). Bushland and open space elements also differed in dispersion and assemblage structure (Tables 1 and 2). There was a significant effect of landscape block within each landscape category, showing that replicate blocks within a category had different assemblages (Table 2).

47

SIMPER analysis revealed that differences between landscape categories were driven by the total absence or lower detection of several taxa from the urban category; including V. vulturnus, C. morio, F. tasmaniensis, Nyctophilus spp., and S. orion. These species were more frequently detected in the suburban landscapes, in

Fig. 5. Regression tree for species activity. (a) Chalinolobus gouldii; (b) Chalinolobus morio; (c) Mormopterus sp. 2; (d) Miniopterus schreibersii oceanensis; (e) Nyctophilus spp.; (f) Tadarida australis; (g) Vespadelus vulturnus. Each split corresponds to criteria which are displayed with the variable causing the spilt (explanatory variable, untransformed data), see Table 1 and Table 2 for description of variables. If the condition is true, proceed to the left branch of the node, otherwise proceed right. Values at the base of each node (vertical lines) represent mean bat activity for each species (log +1). Variable abbreviations follow Appendix A and B.

48

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52 Table 3 Residual mean deviance of each regression tree model, and variance explained (R2).

Fig. 5 (continued)

addition to other infrequently encountered species including M. norfolkensis. Vegetated landscapes also had greater activity of Nyctophilus spp., V. vulturnus, C. morio and R. megaphyllus, where they contributed almost 40% of the similarity in this assemblage. Instead, the urban assemblage was characterized by C. gouldii (50.3%), M. schreibersii oceanensis (24.9%), and T. australis (13.5%). A visual assessment of the nMDS plots (which both have stress = 0.17, adequately, although not perfectly, representing the data) shows that the urban and suburban shale assemblages are most different (Fig. 3a), and the open space assemblage appears to be a subset of the bushland assemblage (Fig. 3b). Backyard and open space elements were characterized by ‘urban’ species listed above, as well as Mormopterus sp. 2, where it contributed 15.2% and 29.7%, respectively to those assemblages. Riparian elements were also characterized by these common species, in addition to rarely encountered species including R. megaphyllus (4%). 3.2. Predictors of species activity Using the CCA we found strong correlations for several species, where the environmental variables explained 24.5% of the total variation among species activity (Fig. 4). M. macropus was strongly correlated with gaps in vegetation and riparian elements, and to a lesser extent vegetation clutter, and was negatively correlated with open space and backyard elements, and total moth biomass. R. megaphyllus was strongly negatively correlated with housing density (houses/ha). S. orion and to a lesser extent Mormopterus sp. 2 and T. australis were moderately correlated with open space and backyard elements, and with total moth biomass. These species and F. tasmaniensis were also correlated with total beetle biomass. V. vulturnus was moderately correlated with increasing sandstone in the landscape and a greater extent of bushland (ha). V. darlingtoni and M. schreibersii oceanensis were moderately correlated with greater shale in the landscape, and were negatively correlated with increasing sandstone and bushland extent. Miniopterus australis, M. norfolkensis were also weakly correlated with vegetation gaps, riparian elements, vegetation clutter and hollow density, and to a lesser extent Nyctophilus spp., and C. morio. S. rueppellii and C. gouldii were situated towards the center of the plot, indicating no strong association with any measured variable, although were weakly associated with shale bushland. For C. gouldii, this reflects its occurrence in a wide range of habitats across the urban landscape. Regression tree modeling of the most commonly recorded species identified several variables as good predictors of species activity (Fig. 5a–g). The density of houses in the landscape had a large

Species

Residual mean deviance

R2

Chalinolobus gouldii Chalinolobus morio Mormopterus sp. 2 Miniopterus schreibersii oceanensis Nyctophilus spp. Tadarida australis Vespadelus vulturnus

1.04 0.13 0.48 0.29 0.34 0.38 0.48

0.49 0.52 0.64 0.63 0.47 0.52 0.68

influence on the activity of several bat species as it was the first split for C. morio and V. vulturnus, and the second to fourth split for Mormopterus sp. 2 and T. australis (Fig. 5a–g). T. australis had greater activity in areas of higher housing density, whereas other species were negatively influenced by increasing housing density, although the threshold value for Mormopterus sp. 2 was higher than for all other species (Fig. 5c and f). The amount of bushland (ha) on shale geology also had a positive influence on Nyctophilus spp. and C. morio activity, but had a negative influence on Mormopterus sp. 2 activity, where the threshold amount of bushland for each species varied between 5 and 21 ha (Fig. 5b, c and e). Activity of Mormopterus sp. 2, T. australis and V. vulturnus increased with increasing biomass of preferred prey items (moths for the first two and beetles for the latter species), however, Nyctophilus spp. activity decreased with increasing moth biomass. The percent of shale or sandstone in the landscape and vegetation gaps accounted for most of the remaining variation in individual species activity. The residual mean deviance and percent deviance explained (R2) are listed in Table 3 for each model, where five species were considered to have adequate models (R2 over 0.5, or 50%) and two with marginally poorer models (R2 0.45–0.5). 4. Discussion We found a diverse bat community across the Sydney landscape; however the long term conservation of such diversity is uncertain. In order to maintain a broader range of species than is currently accounted for by urban planners a more active and strategic approach to conservation planning needs to be taken in managing urban landscapes. Each species differed in their response to urbanization, however many species were found in suburban or vegetated landscapes. This confirms recent Australian findings showing suburban areas support a diverse bat fauna (Basham et al., 2011; Hourigan et al., 2010; Threlfall et al., 2011) including a diversity of threatened species. Indeed, bats are likely to be the most diverse group of mammals remaining in many cities (see Jung and Kalko, 2011; van der Ree and McCarthy, 2005). However, despite this diversity the level of activity we recorded for each species was very low compared to forested and agricultural landscapes (see Law and Chidel, 2006; Lumsden and Bennett, 2005), highlighting that urbanization has a strong negative impact overall (see also Threlfall et al., 2011). 4.1. Bat assemblages along the urban gradient Urbanization played a significant role in structuring bat assemblages. Human-modified landscape elements had a simplified assemblage compared to natural landscape elements, including bushland and riparian habitats. Species absent or with low activity in open spaces included those associated with areas of greater bushland and a higher density of tree hollows in a previous study of ‘leafy’ suburbs in Sydney (Basham et al., 2011), including V. vulturnus, Nyctophilus spp. and C. morio. These species emit high frequency echolocation calls and forage along vegetation edges or

49

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

within clutter. Urban environments have been suggested to impose harsh environmental conditions, thus filtering the types of species occurring, ultimately creating a simplified homogenous fauna (McKinney, 2006). This process is referred to as ‘biotic homogenization’, describing few winning species replacing many losers (McKinney and Lockwood, 1999). Thus, in support of this model, the most common species found in highly urbanized areas were C. gouldii, Mormopterus sp. 2, M. schreibersii oceanensis and T. australis, which are the same species as those found previously in leafier parts of Sydney (Basham et al., 2011), and in other Australian cities (Hourigan et al., 2006; Hourigan et al., 2010; Scanlon and Petit, 2008). These species share traits with other bats commonly encountered in cities around the world, including lower frequency echolocation, and relatively high wing loading and aspect ratio (Avila-Flores and Fenton, 2005; Everette et al., 2001; Gehrt and Chelsvig, 2004; Jung and Kalko, 2011; Kurta and Teramino, 1992). In combination with these studies, our results suggest that urbanization is creating a generic urban bat fauna globally that share similar morphological traits. Similar to other taxa, our results indicate that bats can be classified into groups reflecting their sensitivity or tolerance of urban landscapes, and these groupings reflect similarities in traits and habitat use mirrored in other urban bat assemblages (Jung and Kalko, 2011).

structures in Sydney (Hoye and Spence, 2004; Turton and Hoye, 2011). However, despite the presence of these species across a variety of highly urban habitats, they may still be vulnerable to urban pressures including roost removal/disturbance (Rhodes and Wardell-Johnson, 2006). One trait promoting greater bat activity in more urbanized areas is lower frequency echolocation (Jung and Kalko, 2011; Threlfall et al., 2011), as it promotes prey detection in open areas (Schnitzler and Kalko, 2001). Being able to find prey in open areas is likely to increase the availability of insects to these species across a wider spectrum of urban areas, especially backyards and parklands, where canopy cover has been removed or simplified during the process of urbanization. Both Mormopterus sp. 2 and T. australis activity was positively correlated with prey biomass (moth and beetle, respectively), indicating their ability to track prey may play a role in facilitating their commonness. Morphologically similar species to Mormopterus sp. 2 and T. australis with fast flight and echolocation adapted to detect prey in open areas are known to exploit insects around street lights (Avila-Flores and Fenton, 2005; Blake et al., 1994; Rydell, 1992), suggesting insects there are more available to those species.

4.2. Species tolerant of urbanization

We consider most species in our study to be moderately or highly sensitive to urbanization (Table 4). Several of these species were rarely detected or missing from human modified elements (backyards and open spaces) suggesting they are not likely to cross these matrix components, even if suitable but isolated habitat is present. It must be noted however, that call identification of some sensitive species, including V. darlingtoni is problematic, as calls overlap with M. schreibersii oceanensis (Adams et al., 2010). Thus, the habitat associations reported here for these species should be treated with caution. Broad landscape composition and local structural complexity were important predictors of individual activity. However, vegetation gaps and vegetation clutter were influential for rare species like M. macropus, M. australis and M. norfolkensis. An important variable influencing the activity of many species was geological composition of the landscape, and the extent of bushland on shale geology. Large areas of National Parks, which contain the most vegetation in the study region, are typically located on sandstone (Benson and Howell, 1995; Kartzoff, 1969). Sandstone areas can

Our results indicate a number of taxa are tolerant of moderate levels of urbanization (see Table 4). T. australis activity increased with increasing housing density, and this species as well as Mormopterus sp. 2 were correlated with open space and backyard elements. We found M. schreibersii oceanensis and Mormopterus sp. 2 activity increased with decreasing bushland cover indicating these species are able to exploit the urban matrix for flight activities (see also Basham et al., 2011). These species were found in small urban bushland remnants in addition to backyards and open spaces, thus, they can utilize resources outside the remnant patch. Indeed, previous studies show that C. gouldii is able to utilize urbanized areas as foraging grounds (Kirsten and Klomp, 1998), although were not present in every ‘urban’ element sampled here. C. gouldii and T. australis also utilize human modified areas including parklands, floodplains and golf courses for roosting (Evans and Lumsden, 2011; Rhodes and Wardell-Johnson, 2006) and M. schreibersii oceanensis and T. australis are known to roost in man-made

4.3. Species sensitive to urbanization

Table 4 Classification of species sensitivity to urbanization in Sydney and their conservation status under the New South Wales Threatened Species Conservation Act 1995, and IUCN Red List of Threatened species. Criteria: >20% occurrence overall and >0.5 passes per night in urban category = tolerant; 5–20% occurrence overall; and/or <1 pass per/night or absent from urban category = moderately sensitive; <5% occurrence overall = very sensitive; or insufficient data. Very sensitive species are also those identified as rare in previous studies (Basham et al., 2011). Tolerant species are also those identified as common or tolerant in previous studies (Basham et al., 2011; Hourigan et al., 2006; Hourigan et al., 2010; Scanlon and Petit, 2008). Percent occurrence taken from Threlfall et al. (2011). This assessment is based on bat passes only, and does not account for roost requirements. Response

Species

Conservation status (NSW listing, IUCN listing)

Very sensitive

Falsistrellus tasmaniensis Rhinolophus megaphyllus Myotis macropus

Vulnerable, least concern Not listed, least concern Vulnerable, least concern

Moderately sensitive

Chalinolobus morio Mormopterus norfolkensis Nyctophilus spp. Scotorepens orion Scoteanax rueppellii Vespadelus vulturnus

Not listed, least concern Vulnerable, vulnerable C1 Not listed, not listed Not listed, least concern Vulnerable, least concern Not listed, least concern

Tolerant

Chalinolobus gouldii Miniopterus schreibersii oceanensis Mormopterus sp. 2 Tadarida australis

Not listed, least concern Vulnerable, least concern Not listed, not listed Not listed, least concern

Unknown (insufficient data)

Chalinolobus dwyeri Miniopterus australis Saccolaimus flaviventris Vespadelus darlingtoni

Vulnerable, near threatened Vulnerable, least concern Vulnerable, least concern Not listed, least concern

50

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

be steep, with vegetated gullies, and contain cliffs with caves and overhangs (Howell and Benson, 2000), potentially suitable for cave roosting species that may otherwise be absent from the assemblage. However, cave and non-cave roosting species including Nyctophilus spp., Mormopterus sp. 2 and M. schreibersii oceanensis were negatively associated with increasing sandstone or bushland occurring on sandstone; instead these and other species including C. gouldii and C. morio were positively associated with shale or shale bushland. Bushland cover in areas mainly dominated by sandstone was important for some species including V. vulturnus and R. megaphyllus in our study, and C. morio and V. darlingtoni in a previous study (Basham et al., 2011), indicating it is an important facet of the landscape for these species. However, it appears that the importance of bushland is mediated by geology for other species. The threshold level of bushland on shale geology was 5 ha or more for C. morio and 22 ha or more for Nyctophilus spp., indicating area dependence is species-specific, where Nyctophilus spp. activity did not increase until approximately one third of the radii used for this measure (500 m radius, or 78.5 ha) comprised shale bushland. Greater activity in shale remnants may reflect greater productivity, as these areas support greater insect biomass (correlation between shale bushland and prey biomass in the CCA) and supported greater foraging activity (Threlfall, unpublished results). Despite the fact that many shale remnants are small due to historical patterns of land clearing (Benson and Howell, 1995), our results suggest they are especially important for a range of species, as geology influences habitat quality in this landscape to a greater extent than vegetation cover. 4.4. Recommendations for management of bats within urban landscapes Examining the response of an assemblage to urbanization allows for the assessment of individual species. Some consider the collection of such species specific data as inefficient (Franklin, 1993); however, we argue that collection of data at the community level allows us to make better informed recommendations at a scale relevant for practical conservation planning. Additionally, the categorization of bats by their sensitivity is essential to allow for effective species conservation (Jung and Kalko, 2011). Eventually, suburban areas may occupy the majority of a city’s footprint, and as these areas support a diversity of many species, not only bats (see McKinney, 2008 for a review), it is crucial that active suburban conservation planning occurs. 4.4.1. Conservation of high quality bushland Few species responded to the area of bushland on sandstone, however both tolerant and sensitive species responded to greater amounts of shale or bushland on shale. Even scattered trees in cleared agricultural landscapes are used as foraging and sometimes roosting habitat by bats (Fischer et al., 2010; Law et al., 2000; Lumsden and Bennett, 2005; Lumsden et al., 2002), demonstrating that areas other than large contiguous forest blocks provide essential habitat. We found up to 11 species in one 25 km2 suburban shale landscape with only 12% vegetation cover, as such, a focus on large patches of bushland or areas of greater vegetation cover is unlikely by itself to prove effective for the conservation of many bat species (Fischer et al., 2010). Instead, maintenance of small productive suburban remnants, particularly those on fertile geology should be a management imperative, as patch quality is likely more important than patch size for bats. However, connectivity must also be considered, as open spaces were largely devoid of any sensitive species. Maintaining scattered tree cover in these areas might improve connectivity throughout the matrix, and reduce the isolation of patches. Importantly, the proximity to woodland has been shown to influence bat roost selection (Boughey

et al., 2011a), and species occurrence (Boughey et al., 2011b) in other human altered landscapes. However, radio-tracking studies are needed to identify the extent of movements in the suburban matrix by sensitive species. 4.4.2. Incorporating connectivity into conservation planning Our study suggests that many human-altered elements of the landscape are hostile to sensitive species. In order to maintain these sensitive species, a target for the percent of habitat to be conserved needs to be established. This target is likely to differ between species and by geology; however a general target may at least assist species of conservation concern. We found the activity of sensitive species dwindled with increasing urbanization, to the point that no sensitive species were common in our urban category with less than 10% bushland cover. However, many sensitive species occurred in fertile suburban landscapes where sites averaged between 12 and 28% bushland cover within a 5 km radius, and Nyctophilus spp. activity did not increase until approximately one third of the landscape had shale bushland cover. To meet a threshold level of cover such as 30%, restoration of fertile areas would be necessary; however, young tree plantings have been shown to be of limited value to bats (Law and Chidel, 2006). Prevention of further bushland loss would be the most appropriate strategy and would promote the persistence of sensitive species in suburban areas, including Nyctophilus spp. Planning controls should ensure private land and public open spaces maintain or improve bushland cover. Woodland restoration, such as weed removal and prescribed burning, can positively influence bats (Smith and Gehrt, 2010). Additionally, systematic monitoring using detection methods as described in this study, both before and after specific management interventions occur, would provide an adaptive management framework for assessing the effectiveness of management actions. 4.4.3. Maintaining roosting habitats We found that hollow density was high in riparian elements (Appendix A), suggesting that conservation and restoration of vegetated riparian habitats should conserve important bat roosting habitat. Maintenance of roosting habitat in this way will likely assist in maintaining a variety of species, although more studies, including radio-telemetry, are required to detail roost selection in urban landscapes. Acknowledgements We thank the many field volunteers for their assistance, and private residence and land managers who granted access for field sampling. We also thank T. Penman for providing statistical support and M. Goumas for assistance with sorting insect samples. Useful comments were provided by T. Penman, C. Price, L. Clews and C. Hourigan and three anonymous reviewers. This research was supported by student grants to CT provided by the Royal Zoological Society of NSW and the Ecological Society of Australia. This work was conducted under permission from the OEH. Appendix A and B. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.biocon.2011.11.026. References Adams, M., Reardon, T.R., Baverstock, P.R., Watts, C.H.S., 1988. Electrophoretic resolution of species boundaries in Australian Microchiroptera. IV. The Molossidae (Chiroptera). Australian Journal of Biological Sciences 41, 315–326. Adams, M.D., Law, B.S., French, K., 2005. Effect of lights on activity levels of forest bats: increasing the efficiency of surveys and species identification. Wildlife Research 32, 173–182.

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52 Adams, M.D., Law, B., Gibson, M., 2010. Reliable automation of bat call identification for eastern New South Wales, Australia, using classification trees and AnaScheme software. Acta Chiropterologica 12, 231–245. Aldridge, H.D.J.N., Rautenbach, I.L., 1987. Morphology, echolocation and resource partitioning in insectivorous bats. Journal of Animal Ecology 56, 763–778. Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32–46. Anderson, M.J., Gorley, R.N., Clarke, K.R., 2008. PERMANOVA+ for PRIMER: guide to software and statistical methods. PRIMER-E, Plymouth, UK. Avila-Flores, R., Fenton, B., 2005. Use of spatial features by foraging insectivorous bats in a large urban landscape. Journal of Mammalogy 86, 1193–1204. Basham, R., Law, B., Banks, P.B., 2011. Microbats in a ‘leafy’ Sydney urban landscape: are they persisting, and what factors influence their presence? Austral Ecology 36, 663–678. Benson, D., Howell, J., 1995. Taken for Granted: The Bushland of Sydney and its Suburbs. Kangaroo Press, The Royal Botanic Gardens, Sydney. Blair, R.B., 1996. Land use and avian species diversity along an urban gradient. Ecological Applications 6, 506–519. Blair, R.B., Launer, A.E., 1997. Butterfly diversity and human land use: species assemblages along an urban gradient. Biological Conservation 80, 113–125. Blake, D., Hutson, A.M., Racey, P.A., Rydell, J., Speakman, J.R., 1994. Use of lamplit roads by foraging bats in southern England. Journal of Zoology (London) 234, 453–462. Boughey, K.L., Lake, I.R., Haysom, K.A., Dolman, P.M., 2011a. Effects of landscapescale broadleaved woodland configuration and extent on roost location for six bat species across the UK. Biological Conservation 144, 2300–2310. Boughey, K.L., Lake, I.R., Haysom, K.A., Dolman, P.M., 2011b. Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats. Biological Conservation 144, 1790–1798. Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Austral Ecology 18, 117–143. De’ath, G., Fabricius, K.E., 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192. Development Core Team, R., 2007. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Ethier, K., Fahrig, L., 2011. Positive effects of forest fragmentation, independent of forest amount, on bat abundance in eastern Ontario, Canada. Landscape Ecology 26, 865–876. Evans, L., Lumsden, L.F., 2011. A comparison of the roosting behaviour of Gould’s wattled bats Chalinolobus gouldii using bat boxes and tree hollows in suburban Melbourne. In: Law, B., Eby, P., Lunney, D., Lumsden, L.F. (Eds.), The Biology and Conservation of Australasian Bats. Royal Zoological Society of NSW, Mosman, pp. 288–296. Evelyn, M.J., Stiles, D.A., Young, R.A., 2004. Conservation of bats in suburban landscapes: roost selection by Myotis yumanensis in a residential area in California. Biological Conservation 115, 463–473. Everette, L.A., O’Shea, T.J., Ellison, L.E., Stone, L.A., McCance, J.L., 2001. Bat use of a high-plains urban wildlife refuge. Wildlife Society Bulletin 29, 967–973. Fahrig, L., 1997. Relative effects of habitat loss and fragmentation on population extinction. Journal of Wildlife Management 61, 603–610. Fischer, J., Stott, J., Law, B.S., 2010. The disproportionate value of scattered trees. Biological Conservation 143, 1564–1567. Franklin, J.F., 1993. Preserving biodiversity: species, ecosystems, or landscapes? Ecological Applications 3, 202–205. Gaisler, J., Zukal, J., Rehak, Z., Homolka, M., 1998. Habitat preference and flight activity of bats in a city. Journal of Zoology (London) 244, 439–445. Garden, J., McAlpine, C., Possingham, H., 2010. Multi-scaled habitat considerations for conserving urban biodiversity: native reptiles and small mammals in Brisbane, Australia. Landscape Ecology 25, 1013–1028. Geggie, J.F., Fenton, M.B., 1985. A comparison of foraging by Eptesicus fuscus (Chiroptera: Vespertilionidae) in urban and rural environments. Canadian Journal of Zoology 63, 263–266. Gehrt, S.D., Chelsvig, J.E., 2003. Bat activity in an urban landscape: patterns at the landscape and microhabitat scale. Ecological Applications 13, 939–950. Gehrt, S.D., Chelsvig, J.E., 2004. Species-specific patterns of bat activity in an urban landscape. Ecological Applications 14, 625–635. Germaine, S.S., Wakeling, B.F., 2001. Lizard species distributions and habitat occupation along an urban gradient in Tucson, Arizona, USA. Biological Conservation 97, 229–237. Grimm, N.B., Faeth, S.H., Golubiewski, N.E., Redman, C.L., Wu, J., Bai, X., Briggs, J.M., 2008. Global change and the ecology of cities. Science 319, 756–760. Haberl, H., Wackernagel, M., Krausmann, F., Erb, K.H.K.-H., Monfreda, C., 2004. Ecological footprints and human appropriation of net primary production: a comparison. Land Use Policy 21, 279–288. Holland, G.J., Bennett, A.F., 2007. Occurrence of small mammals in a fragmented landscape: the role of vegetation heterogeneity. Wildlife Research 34, 387–397. Hourigan, C.H., Johnson, C., Robson, S.K.A., 2006. The structure of a micro-bat community in relation to gradients of environmental variation in a tropical urban area. Urban Ecosystems 9, 67–82. Hourigan, C.L., Catterall, C.P., Jones, D., Rhodes, M., 2010. The diversity of insectivorous bat assemblages among habitats within a subtropical urban landscape. Austral Ecology 35, 849–857. Howell, J., Benson, D., 2000. Sydney’s Bushland: More than Meets the Eye. Royal Botanic Gardens, Sydney.

51

Hoye, G., Spence, J., 2004. The large bent-wing bat Miniopterus schreibersii in urban environments: a survivor? In: Lunney, D., Burgin, S. (Eds.), Urban Wildlife: More than Meets the Eye. Royal Zoological Society of New South Wales, Mosman, pp. 138–147. Jung, K., Kalko, E.K.V., 2011. Adaptability and vulnerability of high flying neotropical aerial insectivorous bats to urbanization. Diversity and Distributions 17, 262– 274. Kartzoff, M., 1969. Nature and a City. Edwards and Shaw, Sydney. Kirsten, I., Klomp, N., 1998. Microchiroptera in urban, rural and forest areas of southern NSW. Australasian Bat Society Newsletter 11, 28–31. Krebs, C., 1989. Ecological Methodology. Harper Collins, New York. Kunz, T.H., Lumsden, L.F., 2003. Ecology of cavity and foliage roosting bats. In: Kunz, T.H., Fenton, B. (Eds.), Bat Ecology. The University of Chicago Press, Chicago, pp. 3–89. Kurta, A., Teramino, J., 1992. Bat community structure in an urban park. Ecography 15, 257–261. Law, B.S., Chidel, M., 2006. Eucalypt plantings on farms: use by insectivorous bats in south-eastern Australia. Biological Conservation 133, 236–249. Law, B.S., Anderson, J., Chidel, M., 1999. Bat communities in a fragmented forest landscape on the south-west slopes of New South Wales, Australia. Biological Conservation 88, 333–345. Law, B., Chidel, M., Turner, G., 2000. The use by wildlife of paddock trees in farmland. Pacific Conservation Biology 6, 130–143. Lumsden, L.F., Bennett, A.F., 2005. Scattered trees in rural landscapes: foraging habitat for insectivorous bats in south-eastern Australia. Biological Conservation 122, 205–222. Lumsden, L.F., Bennett, A.F., Silins, J.E., 2002. Location of roosts of the lesser longeared bat Nyctophilus geoffroyi and Gould’s wattled bat Chalinolobus gouldii in a fragmented landscape in south-eastern Australia. Biological Conservation 106, 237–249. Marzluff, J., 2001. Worldwide urbanization and its effects on birds. In: Marzluff, J., Bowman, R., Donnelly, R. (Eds.), Avian Ecology and Conservation in an Urbanizing World. Kluwer Academic Publishers, Norwell, pp. 19–47. McDonnell, M.J., Hahs, A.K., 2008. The use of gradient analysis studies in advancing our understanding of the ecology of urbanizing landscapes: current status and future directions. Landscape Ecology 23, 1143–1155. McDonnell, M.J., Pickett, S.T.A., 1990. Ecosystem structure and function along urban–rural gradients: an unexploited opportunity for ecology. Ecology 71, 1232–1237. McKinney, M.L., 2002. Urbanization, biodiversity, and conservation. BioScience 52, 883–890. McKinney, M.L., 2006. Urbanization as a major cause of biotic homogenization. Biological Conservation 127, 247–260. McKinney, M., 2008. Effects of urbanization on species richness: a review of plants and animals. Urban Ecosystems 11, 161–176. McKinney, M.L., Lockwood, J.L., 1999. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends in Ecology & Evolution 14, 450–453. Meyer, C.F.J., Fründ, J., Lizano, W.P., Kalko, E.K.V., 2008. Ecological correlates of vulnerability to fragmentation in neotropical bats. Journal of Applied Ecology 45, 381–391. Mortelliti, A., Fagiani, S., Battisti, C., Capizzi, D., Boitani, L., 2010. Independent effects of habitat loss, habitat fragmentation and structural connectivity on forestdependent birds. Diversity and Distributions 16, 941–951. Oksanen, J., Blanchet, G., F., Kindt, R., Legendre, P., O’Hara, R.B., Simpson, G.L., Solymos, P., Henry, M., Stevens, H., Wagner, H., 2010. Vegan: Community Ecology Package. R Package Version 1.17-3. . O’Neill, M.G., Taylor, R.J., 1989. Feeding ecology of Tasmanian bat assemblages. Australian Journal of Ecology 14, 19–31. Palmer, M.W., 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology 74, 2215–2230. Pressey, R.L., 1994. Ad hoc reservations: forward or backward steps in developing representative reserve systems? Conservation Biology 8, 662–668. Reinhold, L., Law, B., Ford, G., Pennay, M., 2001. Key to the Bat Calls of South-east Queensland and North-east New South Wales. Queensland Department of Natural Resources and Mines, Brisbane. Rhodes, M., Wardell-Johnson, G., 2006. Roost tree characteristics determine use by the white-striped freetail bat (Tadarida australis, Chiroptera: Molossidae) in suburban subtropical Brisbane, Australia. Austral Ecology 31, 228–239. Rhodes, J.R., Callaghan, J.G., McAlpine, C.A., De Jong, C., Bowen, M.E., Mitchell, D.L., Lunney, D., Possingham, H.P., 2008. Regional variation in habitat – occupancy thresholds: a warning for conservation planning. Journal of Applied Ecology 45, 549–557. Ricketts, T.H., 2001. The matrix matters: effective isolation in fragmented landscapes. American Naturalist 158, 87–99. Ripley, B., 2010. Tree: Classification and Regression Trees. R Package Version 1.0-28. . Rydell, J., 1992. Exploitation of insects around streetlamps by bats in Sweden. Functional Ecology 6, 744–750. Scanlon, A.T., Petit, S., 2008. Effects of site, time, weather and light on urban bat activity and richness: considerations for survey effort. Wildlife Research 35, 821–834. Schnitzler, H.-U., Kalko, E.K.V., 2001. Echolocation by insect-eating bats. BioScience 51, 557–569.

52

C.G. Threlfall et al. / Biological Conservation 146 (2012) 41–52

Shochat, E., Warren, P.S., Faeth, S.H., McIntyre, N.E., Hope, D., 2006. From patterns to emerging processes in mechanistic urban ecology. Trends in Ecology & Evolution 21, 186–191. Smith, D.A., Gehrt, S.D., 2010. Bat response to woodland restoration within urban forest fragments. Restoration Ecology 18, 914–923. Struebig, M.J., Kingston, T., Zubaid, A., Mohd-Adnan, A., Rossiter, S.J., 2008. Conservation value of forest fragments to palaeotropical bats. Biological Conservation 141, 2112–2126. Threlfall, C.G., Law, B., Penman, T., Banks, P.B., 2011. Ecological processes in urban landscapes: mechanisms influencing the distribution and activity of insectivorous bats. Ecography 34, 814–826.

Turton, M., Hoye, G., 2011. The use of a building for breeding by the white-striped freetail-bat Tadarida australis at Newington, Sydney, New South Wales. In: Law, B., Eby, P., Lunney, D., Lumsden, L.F. (Eds.), The Biology and Conservation of Australasian Bats. Royal Zoological Society of NSW, Mosman, pp. 460–463. United Nations Department of Economic and Social Affairs, P.D., 2010. World Urbanization Prospects: The 2009 Revision. United Nations, New York. van der Ree, R., McCarthy, M.A., 2005. Inferring persistence of indigenous mammals in response to urbanisation. Animal Conservation 8, 309–319. Walsh, A.L., Harris, S., 1996. Factors determining the abundance of Vespertilionid bats in Britain: geographical, land class and local habitat relationships. Journal of Applied Ecology 33, 519–529.