More than just a corridor: A suburban river catchment enhances bird functional diversity

Landscape and Urban Planning 157 (2017) 331–342

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More than just a corridor: A suburban river catchment enhances bird functional diversity Jessleena Suri a,b,∗ , Pippin M. Anderson a , Tristan Charles-Dominique c , Eléonore Hellard b,c , Graeme S. Cumming b,d a

Department of Environmental and Geographical Sciences, University of Cape Town, Rondebosch, Cape Town 7701, South Africa Percy FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, Department of Biological Sciences, University of Cape Town, Rondebosch, Cape Town 7701, South Africa c Department of Biological Sciences, University of Cape Town, Rondebosch, Cape Town 7701, South Africa d ARC Centre of Excellence in Coral Reef Studies, Townsville, Queensland 4811, Australia b

h i g h l i g h t s • • • • •

We examined the effect of an urban river catchment on bird taxonomic and functional diversity. In a small area a high diversity of species and functional groups were represented. Certain species and functional groups responded strongly to the presence of the river. Functional composition of the catchment mirrored that of the whole of southern Africa. Habitat heterogeneity within the catchment and the presence of a river enhances bird diversity in the city.

a r t i c l e

i n f o

Article history: Received 18 November 2015 Received in revised form 18 July 2016 Accepted 24 July 2016 Keywords: Urban ecological infrastructure Functional diversity Birds Urban river RLQ Fourth-corner analysis

a b s t r a c t Globally, as trends of urbanisation continue to intensify, there has been increasing concern over the impacts of urban expansion on biodiversity and greater attention towards addressing these impacts. Ecological infrastructure such as urban rivers and their catchments may enhance biodiversity, ecological functioning and ecosystem service delivery within cities. Birds are good indicators of urban habitat quality because their ecology is well-studied and they are habitat selective. This study assesses the ecological value of a small urban river catchment in Cape Town, South Africa, in terms of its effect on the taxonomic and functional diversity of birds. 178 bird counts were carried out at 89 sites and 95 species were recorded. The nine functional groups considered were present in equal proportions in the catchment and in the whole of southern Africa, making the catchment a microcosm of the region’s avifauna in terms of functional composition. Using RLQ and fourth-corner analyses, we showed that the river was responsible for the occurrence of certain species and functional groups that would not otherwise occur in the suburbs. Nutrient movers, insectivores, scavengers and seed dispersers responded strongly to a gradient of distance from the river and the position on the river. Contrary to the homogenised assemblage that might be expected of an urban area, the catchment contains a taxonomically and functionally diverse bird assemblage. The combination of a river, a heterogeneous urban matrix and an adjacent national park makes this catchment an exemplar of the value of ecological infrastructure for urban biodiversity. © 2016 Elsevier B.V. All rights reserved.

1. Introduction

∗ Corresponding author. Present address: Percy FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, Department of Biological Sciences, University of Cape Town, Rondebosch, Cape Town 7701, South Africa. E-mail address: [email protected] (J. Suri). http://dx.doi.org/10.1016/j.landurbplan.2016.07.013 0169-2046/© 2016 Elsevier B.V. All rights reserved.

As the Earth’s urban population continues to grow, conservation of biodiversity within urban environments is becoming increasingly necessary (Seto, Parnell, & Elmqvist, 2013). The impacts of urbanisation on biodiversity include habitat loss and fragmentation, extinction of native species, proliferation of alien species, altered species interactions, increased transmission of wildlife dis-

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eases, changes in productivity, biotic homogenisation, and novel threats such as traffic and predation by pet cats and dogs (Grimm et al., 2008; Goddard, Dougill, & Benton, 2010; McKinney, 2002). Most cities experience a net loss of indigenous biodiversity, ecological function, and ecosystem service delivery. The fundamental challenge for urban conservation is thus to understand how urban landscapes can be developed, remodelled, or restored in ways that support natural processes and the persistence of functional ecosystems. Birds are often chosen as biological indicators or umbrella species for the impacts of urbanisation on ecosystems because their ecology is well known, they are easy to observe and identify, and they respond quickly to changes in habitat and plant community structure (Fontana, Sattler, Bontadina, & Moretti, 2011; Vandewalle et al., 2010; Wenny, DeVault, Johnson, Kelly, & Sekercioglu, 2011). Bird species composition is sensitive to habitat quality because birds are highly mobile and habitat selective (Hostetler, 1999). Birds also make good conservation targets in cities because of their potential ripple effect on broader conservation initiatives: conspicuous and charismatic species may be critical in shaping how urban dwellers experience and identify with nature (Fontana et al., 2011). Most studies along rural-urban gradients have found that bird species richness decreases with increasing levels of urbanisation (Blair, 1996, 2004; Clergeau, Savard, Mennechez, & Falardeau, 1998) primarily due to a decrease in vegetation cover and natural habitat. Bird species richness may peak at intermediate levels of urbanisation, due to the creation of an artificially high diversity of plants and habitats through active landscaping by suburban residents (Alberti, 2005; Cilliers & Siebert, 2012; McKinney, 2002; Savard, Clergeau, & Mennechez, 2000;). Although urban-induced changes in bird species richness are well-documented, little information is available on the biological attributes of bird communities in cities. This limits our understanding of the degree of environmental filtering taking place in cities. For example, we might expect a disproportionate loss of upper trophic level species, such as raptors and scavengers, in urban areas due to trophic reorganisation and collapse (as demonstrated for agricultural areas; Child, Cumming, & Amano, 2009). Analysis of the functional composition of urban bird communities can contribute to a broader understanding of the responses of bird communities to changes in habitat characteristics and community composition (Diaz & Cabido, 2001; Vandewalle et al., 2010). Functional composition in turn reflects the biological integrity and resilience of existing ecological functions to perturbations (Bishop & Myers, 2005; Elmqvist et al., 2003). Networks of urban green spaces facilitate the persistence of species within the urban environment and foster connections between humans and nature which may otherwise be lost to urban residents (Goddard, Dougill & Benton, 2010; Kong, Yin, Nakagoshi, & Zong, 2010). These spaces also ensure that the city is not an impermeable barrier for native biodiversity, “providing green fingers through what would otherwise be urban grey” (Ignatieva, Stewart, & Meurk, 2011). Urban green spaces are often termed ‘ecological infrastructure’: natural elements within the city which simultaneously conserve biodiversity and ecological processes while providing ecosystem services (such as flood control or an aesthetically pleasing recreational environment) to urban residents (Benedict & McMahon, 2002). Rivers and wetlands can be vital components of urban ecological infrastructure. In much of the world, however, urban rivers and their riparian zones have been heavily degraded or transformed. This phenomenon is described as the ‘urban stream syndrome’, whereby chemical, physical and biological elements of the stream are altered by urban development, disturbance, pollution and catchment hardening (Paul & Meyer, 2001; Walsh et al., 2005). Urban streams usually experience reduced invertebrate diversity, altered vegetation structure and instream habitat, and loss of riparian forest (Urban, Skelly, Burchsted, Price, & Lowry,

2006). As a result, urban streams and their catchments are seldom viewed as natural ecosystems, even though they can potentially be local hotspots of ecological function and diversity. The potential of urban streams to act as refuges for urban biodiversity has not been widely explored. A few studies have looked at bird diversity on urban streams (Dallimer et al., 2012; Rodewald & Bakermans, 2006; Rottenborn, 1999) but none have examined how rivers and their catchments impact the functional composition of urban bird communities. We examined the bird community of the Liesbeek River catchment – a small river in suburban Cape Town, South Africa – as an exemplar of how urban rivers and their catchments might be expected to influence the functional composition of urban bird communities. By assessing patterns of bird diversity within the catchment in relation to environmental features, we can ascertain whether and how urban bird communities respond to the presence of a river in a suburban area. In particular, we asked which functional groups were associated with the river; whether and how the bird community of the river’s catchment reflected the ecological functions present in the broader community of South African birds; and whether there were particular environmental features of the catchment that appeared to facilitate the presence of bird diversity in general and ecologically important functional groups in particular. Our results have some interesting and important implications for the conservation of bird taxonomic and functional diversity in urban areas, highlighting how even the presence of a relatively degraded stream can enhance ecological function and biodiversity in the city.

2. Materials and methods 2.1. Study area The Liesbeek River is a small, urban river located in Cape Town, South Africa (Fig. 1). It originates in Table Mountain National Park and runs through several mid- to high-income suburbs before merging with the Black River. The Liesbeek drains the eastern flanks of Table Mountain and is about 9 km long, with a catchment of 327 km2 (Brown & Magoba, 2009). Residential and urban development along the river began in the 19th century, causing the transformation of most of the natural riparian zone and the canalisation of about 40% of the river channel for flood protection (Brown & Magoba, 2009; River Health Programme, 2005). The river also serves as an outlet for storm water drains. Due to its reliance on winter rainfall, the river’s upper and middle reaches remain shallow or dry for much of the year. The lower reaches of the river are deeper and contain permanent water bodies due to their connection with the Black River, a man-made river which is mainly fed by waste water treatment works. The upper reaches of the river are still relatively pristine and support patches of indigenous vegetation and riparian woodland (Fig. 2). The middle section of the river is the biggest and most representative of the Liesbeek as a whole. Here, the river is mostly canalised, constricted by adjacent urban development and highly disturbed, with little indigenous riparian vegetation. The lower reaches of the river are also surrounded by urban and industrial development, but contain more open spaces and wetlands. Much of the river contains exotic and alien invasive species but portions are gradually being rehabilitated with indigenous riparian vegetation (River Health Programme, 2005). Several indigenous vegetation types exist within the Liesbeek catchment (Fig. 3). Five of these occur in the study area: Peninsula Granite Fynbos, Peninsula Shale Fynbos, Peninsula Shale Renosterveld, Cape Flats Dune Strandveld and Cape Flats Sand Fynbos (Mucina & Rutherford, 2006). At present, most indigenous vegetation has been transformed due to urbanisation and many of these

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Fig. 1. Google Earth images showing location of Liesbeek River within Cape Town, South Africa and locations of point count sites within catchment.

Fig. 2. Pictures of the upper, middle and lower reaches of the Liesbeek River (left to right).

vegetation types are endangered or critically endangered (Mucina & Rutherford, 2006).

Table 1 Environmental variables measured at each site during bird counts. Visibility

2.2. Data collection A total of 89 study sites were surveyed within the Liesbeek River catchment (Figs. 1 and 3) between May and September 2014. The river was roughly divided into 19 transects at 500 m intervals from each other, extending about 1 km on either side of the river. Wherever possible, we sampled 5 points on each of these transects, but in several places this design was compromised due to lack of access to private properties. The portion of the catchment on the higher slopes of Table Mountain was excluded due to inaccessibility and because it is within a national park and therefore less disturbed by urbanisation. Each site was surveyed twice in the year to capture both resident and migratory species, with one count in autumn and one in spring, giving a total of 178 individual counts. Each count involved a five minute habituation period followed by a 15 min point count

Weather Disturbance Canalised Predators Alien Species Flowering Plants

Openness of each site estimated on a scale of 0 (<10 m visibility) – 3 (>100 m) Weather conditions during the counting period; 0 (poor due to wind or rain) – 2 (good) Level of disturbance during counting period due to people or traffic; 0 (none) – 2 (heavy) Whether the nearest section of the river was canalised (0) or natural (1) Presence (1) or absence (0) of predators such as cats or dogs Presence (1) or absence (0) of alien plant species Presence (1) or absence (0) of flowering plants

during which all birds seen within a 150 m radius were recorded. Counts were done in the mornings, during periods of maximum bird activity, lasting for roughly four hours from sunrise (Dallimer et al., 2012). The order in which sites were visited was randomised.

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Fig. 3. Map of study sites showing elevation and original cover of indigenous vegetation types in the Liesbeek River catchment.

We measured a variety of environmental variables that might influence bird community composition at each counting point (Table 1). We used ArcGIS to extract the elevation, distance from the Liesbeek river, distance from the source of the river (as a measure of whether a site fell into the upper, middle or lower reaches) and original vegetation type (ENPAT, 2000) for each point. Land cover data were obtained from the National Land Cover (NLC) database for the year 2000 (National Land Cover Consortium, 2005) and extracted using ArcGIS. These data were at a resolution of 30 m per pixel. The following land cover types were present in the study area: ‘thicket, bushland, bush clumps and high fynbos’ (termed as thicket in data analysis), ‘shrubland & low fynbos’ (termed as shrub), ‘planted grassland’ (grass), ‘forest plantations – pine’ (plantation) and ‘urban/built-up’ (urban). Since a site could contain different types of land cover, we calculated the percentage of each land cover type within a 150 m buffer (the same radius within which birds were counted) around each site. Some of these variables were correlated with each other (e.g., elevation is inversely related to the distance from the source, and visibility is positively correlated with grass cover). Such correlations were corrected for in the analysis. 2.3. Species and functional diversity Bird species were allocated to one or more functional groups following the classifications of Cumming and Child (2009) and Sekercioglu (2006), based on foraging ecology data from Hockey et al. (2005). These functional groups capture both the foraging guild of each species and the primary ecological functions that they perform (Sekercioglu, 2006). Nine groups were considered: granivores, grazers, raptors, scavengers, ecological engineers, seed

dispersers, pollinators, nutrient movers and insectivores (Appendix B). It should be noted that our ‘raptors’ group included species not traditionally classified as raptors by birders, such as herons and egrets which feed on fish and frogs (following Cumming and Child, 2009). Since a species may fall into more than one group, they were assigned a 1 or 0 for each functional group. The proportion of the total number of species within each functional group was compared to equivalent data estimated for the whole of southern Africa (Hockey, Dean, & Ryan, 2005) using a Chi-squared test.

2.4. Multivariate analysis We first tested for spatial autocorrelation in the bird count data at different scales using the Mantel correlogram function of the Vegan package in the R software (Borcard, Gillet, & Legendre, 2011; Bivand & Piras, 2015; Oksanen et al., 2013; R Core Team, 2014). No evidence of spatial autocorrelation was found after Holm correction for multiple testing, therefore we did not attempt to correct for autocorrelation in the following analyses. Second, we tested for the potential confounding effects of weather, disturbance and month of each point count using a Mantel test between two dissimilarity matrices: (i) a site x variable matrix describing the confounding variables for each of the 178 counts; the ordinal variables were rank-normalized to make them dimensionfree and the pairwise Euclidean distances between counts were calculated; (ii) a site x species matrix of bird abundances during each count; abundances were log-transformed to decrease the influence of very abundant species and the Bray-Curtis distances between pairs of counts were calculated. Since both data sets were non-normal, the Spearman correlation coefficient was used. The

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correlation was not significant (r = 0.04, p = 0.08) so these three variables were not included in the next analyses. Third, we explored patterns of diversity within the catchment using Hill’s numbers, which provide indices that are robust to disparities in sampling effort in different parts of the catchment (Ludwig and Reynolds, 1988). Hill’s numbers consist of three numbers (Heip, Herman, & Soetaert, 1998; Hill, 1973): H0 (which is equivalent to the number of species), H1 (the exponential of Shannon’s diversity index) and H2 (the inverse of Simpson’s diversity index). We compared diversity between sites near (within 150 m) and away from the river to determine the effect of the river, as well as between different sections of the river. High (>40 m) and low (<40 m) elevation sites were also compared to determine the effect of Table Mountain and the national park. Fourth, we conducted RLQ and fourth-corner analyses using the ade4 package in R version 3.2.2 (Dray & Dufour, 2007; R Core Team, 2014) to investigate the relationships between environmental characteristics and the species composition of each site. RLQ analysis is a powerful tool for predicting species responses to urbanisation (Ikin, Knight, Lindenmayer, Fischer, & Manning, 2012), allowing to examine species abundances over sites, species trait (functional group) distribution over sites, and environmental variable distribution across sites. It is a multivariate technique that provides ordination scores summarising the joint structure amongst three matrices: site x environment (R), site x species (L) and species x trait (Q). After performing an ordination on each matrix individually, each matrix is analysed against each other to create a fourth matrix, an environment x trait matrix (Dray et al., 2014). As our R and Q matrices contained a mix of numerical and categorical data, the dudi.hillsmith function of the ade4 package (Dray & Dufour, 2007) was used to perform a Hill-Smith Principal Component Analysis (PCA). Correspondence Analysis (CoA) was applied to our L matrix using the dudi.coa function of the same package (Dray & Dufour, 2007). RLQ uses a Chi-square distance to reduce the weight of the most abundant species in the analysis. This is particularly revealing in city environments, where urban- exploiting species have a homogenising effect on community composition. A Hellinger transformation was also applied to the species matrix to reduce the effect of large abundance values and preserve the Chi-square distance (Legendre & Gallagher, 2001). RLQ analyses are generally paired with fourth-corner analyses in order to test the hypotheses generated by the RLQ ordinations. The fourth-corner analysis is a robust manner of testing the significance of the associations observed in the ordination of species traits (functional groups) and environmental variables (Dray et al., 2014). The significance of these associations was estimated through 999 permutations and correction for multiple testing using the False Discovery Rate (Benjamini & Hochberg, 1995).

Table 2 Comparison of species diversity at different sections of the river and catchment using Hill’s numbers: H0 (number of species), H1 (exponential of Shannon’s diversity index) and H2 (inverse of Simpson’s diversity index).

Within 150 m of river Away from river Upper reaches Middle reaches Lower reaches Canalised sections Natural sections Elevation <40 m Elevation >40 m Total

No. of Sites

H0

H0 Mean

H0 Std. Dev.

H1

H2

21 68 30 29 30 8 11 51 38 89

64 76 46 47 66 24 51 77 51 95

8.4 6.4 9.3 5.8 5.4 6.4 8.5 8.0 5.3 6.8

5.3 2.8 4.1 2.3 2.9 2.7 5.9 3.9 2.7 3.6

3.5 3.4 3.3 3.1 3.6 2.9 2.9 3.7 3.4 4.0

8.8 8.4 8.4 7.3 10.5 8.4 5.3 11.8 8.8 12.6

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100% 90% Engineers

80%

Scavengers

70%

Raptors

60%

Grazers

50%

Granivores

40%

Insecvores

30%

Nutrient movers

20%

Pollinators

10%

Seed dispersers

0% Catchment

Southern Africa

Fig. 4. Species richness within functional groups for species observed within the Liesbeek catchment and all species in southern Africa as per Cumming and Child (2009). There was no significant difference in proportions between the two samples (Chi-square = 0.09, dF = 8, p = 1).

3. Results 3.1. Species richness and functional diversity Across the 89 sites, 1870 observations of 95 bird species were made. A full list of all species, and their Latin names, is provided in Appendix A. Species diversity was compared for different sections of the river and catchment (Table 2). Species richness was most affected by elevation, whether or not the river was canalised, and the position on the river; whereby low elevation sites in the lower reaches of the catchment and natural sections of the river had the highest number of species. All nine functional groups were represented in our study sites, with insectivores and nutrient movers represented by the highest number of species (53 and 39 respectively). Intriguingly, despite the small proportion of the South African bird fauna represented in our counts, the relative numbers of species in each functional group were statistically indistinguishable from those of the southern African region as a whole (Fig. 4; dF = 8, Chi-square = 0.09, p = 1). 3.2. Multivariate analyses 3.2.1. RLQ and fourth-corner analyses Most of the variation in these analyses were captured by the first and second axes (Appendix C and D). Considering the three matrices together in the RLQ analysis, we could explain 95.66% of variation with the first two axes. The percentages reported in brackets for the individual ordinations refer to the amount of variance preserved in the RLQ compared to the individual ordinations. The high percentages preserved by the RLQ indicate that it sufficiently describes the three matrices in order to maximise environmenttrait covariation. In each of the three ordinations and on the RLQ ordination, the fact that the majority of the variation is captured by Table 3 Results of RLQ analysis of environmental variables (R), species composition (L) and species traits (functional groups; Q). Percentages reported in brackets correspond to the amount of variance preserved in the RLQ analysis compared to separate ordinations. Correlation Axis 1

Correlation Axis 2

RLQ axis eigenvalues RLQ covariance L (CoA)

1.84/86.46% 1.36 0.45 (52.67%) Inertia Axis 1

0.20/9.20% 0.44 0.26 (32.07%) Co-inertia axes 1 & 2

R (Hill-Smith PCA) Q (Hill-Smith PCA)

3.50 (96.57%) 2.60 (92.56%)

6.10 (92.37%) 3.68 (85.01%)

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Fig. 5. Ordination showing relative contributions of environmental variables and species traits (functional groups) to the RLQ axes (Axis 1–86.46%, Axis 2–9.20%); vegetation types – PGF: Peninsula Granite Fynbos, CFSF: Cape Flats Sand Fynbos, PSF: Peninsula Shale Fynbos, PSR: Peninsula Shale Renosterveld.

the first axes suggests that these axes represent an environmental gradient determining patterns of species composition (Table 3). The positions of the environmental variables and functional groups in Fig. 5 indicate their relative contributions to the RLQ axes and the gradients which they represent (Ikin et al., 2012). The first axis represents a gradient defined by elevation, distance from the source of the river, distance from the river, visibility and grass cover. In other words, these are the strongest environmental determinants of the distribution of functional groups within the catchment. The functional groups which were most strongly aligned with this gradient were the nutrient movers, insectivores and pollinators. The fourth-corner analysis tested the strength of the relationships observed in Fig. 5 in order to determine which were most

significant (Fig. 6; Appendix D3). The distance from the river, distance from the source, visibility (or “openness” of a site), elevation, urban and grass cover were revealed as the most important determinants of the distribution of certain functional groups. Of the significant correlations revealed by the fourth-corner analysis, the strongest were those between nutrient movers and the distance from the river, distance to source, elevation and visibility – suggesting a strong relation between the abundance of nutrient movers and their location in the catchment. Insectivores and seed dispersers were positively correlated with distance from the river, suggesting they were not dependent on riparian habitat, unlike nutrient movers and scavengers.

Fig. 6. Fourth-corner summary of significant relationships between functional groups and selected environmental variables. White: significant negative correlation, black: significant positive correlation, grey: insignificant correlation (Vegetation types – PGF: Peninsula Granite Fynbos, CFSF: Cape Flats Sand Fynbos, PSF: Peninsula Shale Fynbos, PSR: Peninsula Shale Renosterveld and CFDS: Cape Flats Dune Strandveld).

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4. Discussion Urban areas are thought to promote the homogenisation of biodiversity by simplifying habitats and creating sterile landscapes which are either unsuitable for most species or which only favour a handful of dominant, urban-exploiting species (Melles, Glenn, & Martin, 2003). Instead, in this study we found that the combination of a river, a heterogeneous urban area and an adjacent national park makes the Liesbeek catchment surprisingly biodiverse for a small and highly disturbed urban river catchment, both in terms of species richness and functional diversity. The contribution of these features to local biodiversity highlights the importance of green space networks and ecological infrastructure in enhancing urban ecosystems. In this case, the river in particular is responsible for the occurrence and persistence of certain species and functional groups. A total of 95 species were observed in this study, but over 120 bird species are known to occur within the 327 km2 study area. In comparison, at least 367 bird species occur within the greater City of Cape Town (Holmes, Rebelo, Dorse, & Wood, 2012). Thus, in an area of less than a sixteenth of the city’s size, the Liesbeek catchment represents almost a third of all the species which occur within the city. While the connectivity and proximity between natural patches in the catchment help to increase species richness, increased habitat heterogeneity also provides more niches for specialist species (Fernandez-Juricic & Jokimaki, 2001; Shanahan, Miller, Possingham, & Fuller, 2011). The catchment was also revealed to be rich in terms of functional diversity, with all nine groups represented. These groups were present in almost the same proportions as in the bird community of the whole of southern Africa. Moreover, while urban areas are expected to experience a relative decline in the number of insectivores, pollinators, raptors and scavengers (Child et al., 2009; Faeth, Bang, & Saari, 2011), all of these groups were well-represented within the catchment. Given that invertebrate abundance can be severely affected by human disturbance, the presence of several uncommon smallbodied insectivores is a particularly good sign of ecosystem health (Larsen, Sorace, & Mancini, 2010). Raptors are also significant for ecosystem functioning because each species, though superficially similar, may occupy distinct niches and can also act as umbrella species (Palomino & Carrascal, 2007; Sekercioglu, 2006). In this study, almost every raptor species known to occur locally was recorded. Our results might be interpreted as indicating a surprising degree of resilience in the functional composition of the bird community. If we consider the mechanisms underlying community structure, much of the observed diversity in this study can be directly attributed to the river. Of the 95 species recorded in the catchment, 64 species were recorded on the river itself, even though less than a third of the sites were located on the river. Although surrounded by an industrial area and several major highways, species richness was highest at the lower reaches of the river. Bird communities in these sections of the catchment were distinct in that they were strongly shaped by the presence of the two rivers and associated wetlands. The RLQ and fourth-corner analyses pointed to certain functional groups favouring this area. For example, nutrient movers and scavengers appeared to respond positively to areas of low elevation, high visibility, close to the river and far from the river’s source – all characteristics of this particular section of the river. Despite covering a small area in the catchment, this portion of the river and catchment is clearly a very important feature in the landscape for birds. It contains a bird sanctuary and open stretches of wetland where the Liesbeek and Black Rivers meet, and an abundance of waterfowl and aquatic species. This supports previous studies showing that richness of aquatic species increases with higher river discharge (Xenopoulous & Lodge, 2006)

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and that wetlands support greater bird richness than adjacent uplands (Robinson, Tockner, & Ward, 2002; McKinney, Raposa, & Cournoyer, 2011). The river contributes to creating a more “complete” assemblage of species by bringing in guilds of species which thrive in aquatic and riparian areas. The presence of a variety of functional groups is not only a good indicator of the health of a biotic community, but also signifies the degree to which various ecological functions are being performed within an area. For example, the river brings in nutrient movers, a functional group consisting primarily of aquatic birds such as waterfowl. Species such as these are described as ‘mobile links’ and can be vital to ecological functioning as they connect aquatic and terrestrial habitats, transporting nutrients and fertilising various habitats while dispersing plants and invertebrates up and downstream (Sekercioglu, 2006; Reynolds, Miranda, & Cumming, 2015). Similarly, the occurrence of three species of Kingfisher in the catchment is important given that they are ecosystem engineers which can create habitat for other organisms (e.g., reptiles, amphibians and insects) through their nesting burrows. The presence of charismatic species such as these can also renew public perception of the river as an ecologically functional system and spark interest in its rehabilitation and conservation. This was for instance seen when Greater Flamingos recently returned to the area where the Liesbeek and Black Rivers meet. Although we focused on the effect of the river on bird diversity in the catchment, it is evident that there are many more environmental influences, particularly since each side of the river offers different habitats, with one side flanked by Table Mountain and the other side mainly urbanised. For example, it has been shown that adjacent urban development exerts a negative impact on riverine birds, due to disturbance and the loss of riparian habitat (Rodewald & Bakermans, 2006; Rottenborn 1999; Smith & Wachob, 2006). On the Liesbeek River, pollution, disturbance and urban development in the surrounding matrix could have negative impacts on bird diversity which may outweigh positive influences of other habitat features. This was observed in the middle, canalised sections of the river which are constricted by urban development and had the lowest diversity of species. On the other hand, it was clear that patches of remnant vegetation also exerted a strong influence on suburban bird communities in the catchment. Proximity to Table Mountain National Park and indigenous vegetation marked the presence of different bird assemblages. Similarly, at a site located on one of the last remaining patches of Cape Flats Sand Fynbos vegetation, three species were encountered which were seen nowhere else in the study area – Cape Longclaw, Zitting Cisticola, and Cloud Cisticola. This again points to the importance of small, high quality patches in the catchment. In the case of wetland passerines, management and conservation of small habitat patches such as these was found to be more important for population persistence than characteristics of the surrounding matrix (Calder, Cumming, Maciejewski, & Oschadleus, 2015). While the presence of several confounding factors may complicate our ability to isolate and predict the effects of landscape features such as rivers on urban biodiversity, they do reveal a great deal about the importance of landscape heterogeneity in shaping biotic communities in cities. Case studies such as this help to identify landscape features which exert the strongest influence on urban biodiversity and can have important implications for rehabilitation and conservation within urban areas. For example, it is clear from this study that the combination of ecological infrastructure within the Liesbeek catchment plays a vital role in enhancing local bird diversity and the ecological functions performed by birds in the area. Urban rivers like the Liesbeek have long been viewed as dirty and defunct, to the extent that they are often regarded as glorified storm water drains and have historically been managed as such. The bird diversity encountered in this study can strengthen local perceptions of the

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river as an ecologically functional unit and highlight its potential conservation value, with both the birds and the river itself providing valuable ecosystem services. These findings can be extended to river catchments in other cities, which face a variety of major challenges such as storm water management, development pressure, pollution, instream heterogeneity, riparian health and invertebrate and fish diversity. Our findings have the potential to draw attention to the biodiversity of river catchments in urban conservation plans and will contribute to creating public interest and mobilising action towards integrated catchment management and restoring urban rivers to healthy, functional ecosystems.

Acknowledgements We would like to thank David Nkosi for his support with bird counts, as well as Doug Harebottle and Nick Fordyce for assisting with data collection and compilation of the catchment bird list. We would also like to thank Nicholas Lindenberg and Thomas Slingsby for their assistance with GIS. Appendix A. See Tables A1 and A2.

Table A1 List of species recorded (including records of species that were only heard or seen flying over). Common Name

Scientific Name

Functional Groups

No. of sites

African Black Duck African Black Swift African Darter African Dusky Flycatcher African Fish-Eagle African Goshawk African Harrier-Hawk African Olive-Pigeon African Sacred Ibis Alpine Swift Amethyst Sunbird Bar-throated Apalis Black-headed Heron Black Sparrowhawk Blacksmith Lapwing Bokmakierie Brimstone Canary Brown-throated Martin Cape Batis Cape Bulbul Cape Canary Cape Longclaw Cape Robin-Chat Cape Shoveler Cape Siskin Cape Sparrow Cape Spurfowl Cape Turtle-Dove Cape Wagtail Cape Weaver Cape White-eye Cattle Egret Cloud Cisticola Common Chaffinch Common Fiscal Common Moorhen Common Starling Common Waxbill Egyptian Goose Forest Buzzard Forest Canary Fork-tailed Drongo Giant Kingfisher Great White Pelican Greater Flamingo Greater Striped Swallow Grey-headed Gull Grey Heron Hadeda Ibis Hartlaubs Gull Helmeted Guineafowl House Sparrow Jackal Buzzard Karoo Prinia Kelp Gull Klaas’ Cuckoo Knysna Warbler Laughing Dove Lesser Swamp-Warbler Levaillants Cisticola Little Egret

Anas sparsa Apus barbatus Anhinga rufa Muscicapa adusta Haliaeetus vocifer Accipiter tachiro Polyboroides typus Columba arquatrix Threskiornis aethiopicus Tachymarptis melba Chalcomitra amethystina Apalis thoracica Ardea melanocephala Accipiter melanoleucus Vanellus armatus Telophorus zeylonus Crithagra sulphuratus Riparia paludicola Batis capensis Pycnonotus capensis Serinus canicollis Macronyx capensis Cossypha caffra Anas smithii Crithagra totta Passer melanurus Pternistis capensis Streptopelia capicola Motacilla capensis Ploceus capensis Zosterops virens Bubulcus ibis Cisticola textrix Fringilla coelebs Lanius collaris Gallinula chloropus Sturnus vulgaris Estrilda astrild Alopochen aegyptiaca Buteo trizonatus Crithagra scotops Dicrurus adsimilis Megaceryle maximus Pelecanus onocrotalus Phoenicopterus ruber Hirundo cucullata Larus cirrocephalus Ardea cinerea Bostrychia hagedash Larus hartlaubii Numida meleagris Passer domesticus Buteo rufofuscus Prinia maculosa Larus dominicanus Chrysococcyx klaas Bradypterus baboecala Streptopelia senegalensis Acrocephalus gracilirostris Cisticola tinniens Egretta garzetta

Nutrient mover, grazer Insectivore Nutrient mover, raptor Insectivore Nutrient mover, raptor, scavenger Raptor Raptor, scavenger Seed disperser Nutrient mover, raptor, scavenger Insectivore Pollinator, insectivore Insectivore Insectivore, raptor Raptor Nutrient mover, insectivore Seed disperser, insectivore Seed disperser, pollinator, granivore Nutrient mover, insectivore Insectivore Seed disperser, pollinator, insectivore Seed disperser, granivore Nutrient mover, insectivore Seed disperser, insectivore Nutrient mover, grazer Pollinator, granivore Seed disperser, pollinator, insectivore, granivore, grazer Grazer Granivore Nutrient mover, insectivore Pollinator, nutrient mover, insectivore Seed disperser, pollinator, insectivore Insectivore Insectivore Seed disperser, insectivore, granivore Insectivore, scavenger Insectivore, scavenger Nutrient mover, grazer, scavenger Nutrient mover, insectivore, granivore, grazer Nutrient mover, grazer Insectivore Granivore Pollinator, insectivore Nutrient mover, insectivore, raptor, engineer Nutrient mover, raptor, scavenger Nutrient mover, insectivore Nutrient mover, insectivore Nutrient mover, raptor Nutrient mover, scavenger Nutrient mover, insectivore Nutrient mover, raptor, scavenger Grazer Seed disperser, pollinator, insectivore, granivore, grazer Raptor, scavenger Insectivore Nutrient mover, raptor, scavenger Pollinator, insectivore Nutrient mover, insectivore Granivore Nutrient mover, insectivore Nutrient mover, insectivore Nutrient mover, raptor

8 2 4 31 1 11 1 2 33 2 5 2 2 8 12 4 5 3 6 37 60 2 37 6 3 24 4 10 8 13 87 6 2 11 15 5 50 6 65 13 1 2 4 1 1 1 1 3 77 45 20 3 3 18 16 1 2 22 6 9 2

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Table A1 (Continued) Common Name

Scientific Name

Functional Groups

No. of sites

Little Grebe Little Swift Malachite Kingfisher Malachite Sunbird Mallard Neddicky Olive Thrush Peregrine Falcon Pied Crow Pied Kingfisher Red-billed Teal Red-eyed Dove Red-faced Mousebird Red-knobbed Coot Red-winged Starling Reed Cormorant Rock Dove Rock Kestrel Rock Martin Rufous-chested Sparrowhawk Sombre Greenbul Southern Boubou Southern Double-collared Sunbird Southern Masked-Weaver Southern Red Bishop Speckled Pigeon Spotted Thick-knee Steppe Buzzard Swee Waxbill Swift Tern White-backed Duck White-breasted Cormorant White-necked Raven White-rumped Swift White-throated Swallow Yellow-billed Duck Yellow Canary Zitting Cisticola

Tachybaptus ruficollis Apus affinis Alcedo cristata Nectarinia famosa Anas platyrhynchos Cisticola fulvicapilla Turdus olivaceus Falco peregrinus Corvus albus Ceryle rudis Anas erythrorhyncha Streptopelia semitorquata Urocolius indicus Fulica cristata Onychognathus morio Phalacrocorax africanus Columba livia Falco rupicolus Hirundo fuligula Accipiter rufiventris Andropadus importunus Laniarius ferrugineus Cinnyris chalybeus Ploceus velatus Euplectes orix Columba guinea Burhinus capensis Buteo vulpinus Coccopygia melanotis Sterna bergii Thalassornis leuconotus Phalacrocorax lucidus Corvus albicollis Apus caffer Hirundo albigularis Anas undulata Crithagra flaviventris Cisticola juncidis

Nutrient mover, insectivore, raptor Insectivore Nutrient mover, insectivore, raptor Pollinator, insectivore Nutrient mover, grazer Pollinator, insectivore Seed disperser, insectivore Raptor Seed disperser, pollinator, insectivore, scavenger Nutrient mover, raptor, engineer Nutrient mover, grazer Granivore Seed disperser, pollinator Nutrient mover, grazer, scavenger Seed disperser, pollinator, insectivore, scavenger Granivore Granivore Insectivore, raptor Insectivore Raptor Seed disperser, pollinator Pollinator, insectivore, raptor Pollinator, insectivore Pollinator, nutrient mover, insectivore, granivore Nutrient mover, granivore Granivore, grazer Insectivore Insectivore Granivore Nutrient mover, raptor Nutrient mover, grazer Nutrient mover, raptor Seed disperser, insectivore, raptor, scavenger Nutrient mover, insectivore Nutrient mover, insectivore Seed disperser, pollinator, granivore Nutrient mover, grazer Insectivore

4 2 1 10 2 1 52 2 45 2 3 74 1 7 68 9 34 1 2 6 21 7 63 13 1 21 2 9 12 9 1 7 6 1 4 13 1 2

Table A2 Other species known to occur in the Liesbeek catchment. Common Name

Scientific Name

Functional Groups

African Paradise Flycatcher African Spoonbill African Swamphen African Wood Owl Black-crowned Night Heron Black-shouldered Kite Black-winged Stilt Cape Sugarbird Dideric Cuckoo Fulvous Whistling Duck Glossy Ibis Hottentot Teal Lemon Dove Little Bittern Orange-breasted Sunbird Pin-tailed Whydah Purple Heron Three-banded Plover Spotted Eagle Owl Southern Pochard Water Thick-knee White-backed Mousebird White-faced Whistling Duck Yellow-billed Egret Yellow-billed Kite

Terpsiphone viridis Platalea alba Porphyria madagascariensis Strix woodfordii Nycticorax nycticorax Elanus caeruleus Himantopus himantopus Promerops cafer Chrysococcyx caprius Dendrocygna bicolor Plegadis falcinellus Anas hottentota Aplopelia larvata Ixobrychus minutus Anthobaphes violacea Vidua macroura Ardea purpurea Charadrius tricollaris Bubo africanus Netta erythrophthalma Burhinus vermiculatus Colius colius Dendrocygna viduata Egretta intermedia Milvus aegyptius

Insectivore Nutrient mover, insectivore, raptor Nutrient mover, grazer, scavenger Insectivore, raptor Nutrient mover, raptor Raptor Nutrient mover, insectivore Pollinator, insectivore Insectivore Nutrient mover, grazer Nutrient mover, insectivore Nutrient mover, grazer Granivore Nutrient mover Pollinator, insectivore Insectivore, granivore Nutrient mover, raptor Nutrient mover, insectivore Insectivore, raptor, scavenger Nutrient mover, grazer Nutrient mover, insectivore Seed disperser, pollinator Nutrient mover, grazer Nutrient mover, raptor Raptor, scavenger

*Bronze Mannikin (Spermestes cucullatus) – local vagrant, potential escapee. *Snowy Egret (Egretta thula) – vagrant.

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Appendix B.

Appendix D. See Tables D1–D3 .

See Table B1.

Table B1 Definition of the nine bird ecological functional groups. Functional Group

Definition

Insectivores Seed dispersers

Insect-eating birds Fruit-eating birds which transport seeds which pass through their digestive tracts Seed-eating birds Birds of prey Nectarivores Feeding on carrion or waste Aquatic birds which move between land and water, transporting nutrients between the two habitats Eating grass Digging burrows or cavities, creating habitat for other species

Granivores Raptors Pollinators Scavengers Nutrient movers Grazers Ecosystem engineers

Appendix C. See Fig. C1.

Table D1 RLQ axis scores for environmental variables (Vegetation types – PGF: Peninsula Granite Fynbos, CFSF: Cape Flats Sand Fynbos, PSF: Peninsula Shale Fynbos, PSR: Peninsula Shale Renosterveld).

Dist. from river Dist. from source Elevation CFDS CFSF PGF PSF PSR Canalised Visibility Aliens Flowering plants Predators Thicket Shrub Grass Urban Plantation

Fig. C1. RLQ ordination of species distribution along environmental gradients.

Axis 1

Axis 2

−0.46 0.83 −0.74 2.37 0.44 −1.09 −0.54 0.51 −0.09 0.66 0.27 −0.23 −0.23 0.17 0.43 0.56 −0.42 −0.31

−0.12 0.49 −0.60 −1.45 −0.29 −0.71 −0.31 0.91 −0.46 −0.43 −0.21 −0.01 0.19 −0.66 −0.32 −0.09 0.72 −0.41

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Table D2 RLQ axis scores for functional groups.

Seed Dispersers Pollinators Nutrient Movers Insectivores Granivores Grazers Raptors Scavengers Engineers

Axis 1

Axis 2

−0.71 −0.73 0.78 −0.68 0.05 0.66 0.24 0.34 0.19

0.41 0.27 −0.27 −0.30 0.51 0.31 −0.39 0.41 −0.30

Table D3 Strengths of fourth-corner correlation coefficients (* −p < 0.05, ** − p < 0.01, *** − p < 0.005). White: significant negative correlation (p < 0.05), black: significant positive correlation (p < 0.05), grey: insignificant correlation (p > 0.05). Vegetation types – PGF: Peninsula Granite Fynbos, CFSF: Cape Flats Sand Fynbos, PSF: Peninsula Shale Fynbos, PSR: Peninsula Shale Renosterveld.

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