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The confounding influence of homogenising invasive species in a globallyendangered and largely urban biome: Does habitat quality dominate avianbiodiversity?

Simon G. Dures *, Graeme S. CummingPercy FitzPatrick Institute, DST/NRF Center of Excellence, University of Cape Town, Rondebosch 7701, Cape Town, South Africa

a r t i c l e i n f o

Article history:Received 23 June 2009Received in revised form 30 November 2009Accepted 18 December 2009Available online 8 February 2010

Keywords:FynbosLandscape ecologyIsland biogeographyFragmentationBirdUrban

a b s t r a c t

There are many variables within an urban landscape that can be expected to influence faunal diversity.We used a mechanistic approach to observe the relative importance of each of these variables in describ-ing the diversity of avian communities in urban and peri-urban areas of Cape Town, South Africa situatedwithin Cape Floristic Region, a global plant biodiversity hotspot. We expected to find a strong influence ofhabitat amount and arrangement across a fragmented urban–rural gradient. Birds were sampled inpatches of fragmented Cape Flats Sand Fynbos at 39 locations during the middle of summer (Novem-ber–January 2008/2009). Environmental data included on-site observations, existing GIS layers, andremotely sensed data. Bird community composition was related to habitat amount, arrangement, andquality. Correlations, partial correlations and structural equation analysis showed that habitat amountand arrangement had insignificant effects on species richness across the urban gradient. Changes in urbandensity and the presence or absence of the invasive alien tree species Acacia saligna (blue wattle)appeared to be the main drivers of avian species richness. Increasing urban density, decreasing A. salignadensity and decreasing canopy height correlated with higher avian species richness, with confoundingvariables corrected for. For the management of urban biodiversity, our results suggest that the mosturgent focus should be on retaining the quality remaining areas of native vegetation (and particularly,containing the spread of A. salgina) rather than extending current networks with poor quality habitat.More generally, habitat quality appears to be more important than habitat amount or arrangement in thissystem. We suggest that this observation be encorporated into further research to more effectively pre-dict avian responses to landscape change and that more must be done to adequately capture the criticaland complex role of the matrix in terrestrial systems.

! 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Land use change and habitat modification have been identifiedas a key drivers of biodiversity loss (e.g., McKinney, 2002; Filippi-Codaccioni et al., 2008; Meyer and Kalko, 2008; Sanford et al.,2008). They are especially severe in areas of rapid urbanisation.In the United States, urbanisation endangers more species thanany other human activity (Czech et al., 2000), resulting in an envi-ronment with a significantly altered compositional heterogeneity,geometrical complexity and pattern of ecological fragmentation(Anderson, 2006). As the urban landscape becomes more expansiveand more developed and the remaining fragments of native habitatshrink and become more isolated, understanding the ecologicalrole of the remaining fragments becomes increasingly important

for biodiversity conservation. Ultimately, the ability of a speciesor a community to survive in fragmented landscapes will governthe fate of much of the world’s biota in those places where it comesinto conflict with human dominated landscapes (Laurance et al.,2002).

Theories about the ecology of landscape fragmentation are lar-gely based on the theory of island biogeography (MacArthur andWilson, 1967; Watson, 2002). Island biogeography predicts thatthe larger and more connected a fragment is to other fragmentsof the same habitat type, the greater the biotic diversity of thatfragment will be. Flather and Bevers (2002) reviewed fragmenta-tion literature and simulated a fragmented landscape, concludingthat in landscapes with a habitat coverage of less than 30–50% ofthe total landscape, the arrangement of the remaining patchesaccounted for 33–39% of the variation in population size whilehabitat amount accounted for 27–49%.

Urban biogeography, however, is not entirely straightforward,needing take into account the heterogeneity of anthropogenic

0006-3207/$ - see front matter ! 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.biocon.2009.12.019

* Corresponding author.E-mail addresses: [email protected] (S.G. Dures), graeme.cumming@

uct.ac.za (G.S. Cumming).

Biological Conservation 143 (2010) 768–777

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Biological Conservation

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urban environments with respect to changes in vegetation types,dispersal, or associated changes in ecosystem functioning; thereis also, currently, little information in the literature describingthe mechanisms for changes in community composition or speciesvulnerability (Laurance, 2008). One of the major elements likely tobe particularly important in urban ecology is the relevance of patchquality for the variations observed in local populations, where nat-ural remnant vegetation is likely to suffer from increased levels oferosion, trampling, pollution, mowing or pruning, invasion andother human induced disturbances (McKinney, 2002). For example,the immigration of invasive alien species established throughanthropogenic mechanisms can be one of the dominating factorsinfluencing urban ecosystem diversity and functions (Mooneyand Hobbs, 2000). Introduced species are responsible for more birdextinctions than any other documented cause and constitute thesecond most common cause of mammal extinctions (Clavero andGarcía-Berthou, 2005). Invasive species are also responsible formany ecological and evolutionary changes through biotic homoge-nisation, defined as ‘‘the gradual replacement of native biotas bylocally expanding non-natives. . . diminishing floral and faunal dis-tinctions among regions” (Olden et al., 2004). For example, Cler-geau et al. (2006) found that such biotic homogenisation of theEuropean avifauna was closely correlated with urbanisation, withdecreased species richness and fewer species dominating the aviancommunity within cities. Such changes in human dominated land-scapes will also have implications for ecosystem services. Forexample, increases in the phosphorus concentrations found in soil,by application of fertilizer, have ramifications for freshwater eutro-phication, nutrient transport and landscape pattern throughchanges in ecosystem productivity (Bennett et al., 2005).

Many studies have observed local decreases in diversity withincreasing urbanisation (Shochat et al., 2006). Such decreases areoften associated with the replacement of native vegetation by aproliferation of synanthropic species (i.e., those commonly associ-ated with humans) which can thrive in the conditions provided byhuman settlement (Donnelly and Marzluff, 2004). However, otherstudies have found increased richness, diversity and abundancein suburban and peri-urban sites (those found on the urban fringe)relative to more rural areas (McKinney, 2002, 2008; Leveau and Le-veau, 2004). Comparing these contrasting observations, Marzluff(2001) found that from a set of 51 studies, 61% showed a lowerspecies richness amongst urban settlement; this result is by nomeans conclusive.

Without understanding the mechanisms by which urbanisationalters an ecosystem, or a fragment of an ecosystem, the patternssuggested by traditional ecological theory can not be used to makepredictions. The Millennium Ecosystem Assessment (MA; Millen-nium Ecosystem Assessment, 2005) highlighted a number of prior-ities that require further research to fully understand the roleecosystems play in maintaining the sustainability and resilienceof social systems and economies. Broadly speaking, they highlightthe need for landscape ecology and our understanding of biodiver-sity processes to become more mechanistic and less pattern based(Lindenmayer and Fischer, 2006; Cumming, 2007). One prioritythat was identified by the MA is to develop the ability to predictchanges in the provision of ecosystem services as habitat is lostand biodiversity is altered.

This study tests our understanding of the mechanisms behindalterations in functional and species diversity driven by urban frag-mentation. We explored how, and to what degree, urban-ecologi-cal landscape variables (such as location, patch characteristics,and context) influence the functional and species composition ofavifauna within fragmented natural remnants. Focusing on CapeFlats Sand Fynbos, a globally critically endangered vegetation typewithin the City of Cape Town (Holmes et al., 2008), we studied thecommunity composition of birds at 39 sites across an urban–rural

gradient and explored the relevance of spatially explicit variables(such as context, location, and connectivity) in a mechanistic ap-proach to predicting community composition.

2. Study area and methods

2.1. Study area

Cape Town is a coastal city of 3.2 million people, with anexpanding municipal area that currently covers 2487 km2. The citycontains a diverse mix of cultures and economic backgrounds.There is a dramatic divide between rich and poor and its mostpressing socioeconomic problems include poverty, housing short-ages, crime, and the impacts of HIV/Aids and tuberculosis. The bio-physical environment is characterised by a diverse range of soiltypes and topography. The City of Cape Town recognizes 19 differ-ent soil types, and the city is dominated by Table Mountain, a mas-sif nearly 1 km high. Cape Town has a Mediterranean climate and isthe only winter rainfall region in sub-Saharan Africa. Cape Town insituated within the Cape Floristic Region. The region is renownedfor the diversity and endemism of its plant community, knownas the Cape Floristic Region, and comprises the smallest of six glob-ally recognised floral kingdoms (Holmes et al., 2008). Despite beingone of the worlds ‘‘hotspots for conservation priorities” (Myers etal., 2000) and having about 70% endemism in the vascular plantcommunity alone, the protected areas network in the Cape FloristicRegion does not adequately conserve the region’s biodiversity(Rouget et al., 2003). There is thought to be insufficient habitat se-cured and appropriately managed to support successful biodiver-sity conservation in the region.

To control for differences, a single vegetation type was selectedfor the study; Cape Flats Sand Fynbos (Rebelo et al., 2006). CapeFlats Sand Fynbos is endemic to the lowlands of Cape Town, withonly 77 km2 still remaining, mostly in small fragments (Fig. 1),and a national ecosystem status of ‘critically endangered’ is ac-corded (Holmes et al., 2008). In addition to its dire need for effec-tive conservation, Cape flats Sand Fynbos was chosen because of abroad gradient in remaining fragment sizes. It occurs on mostly flatplains on leached, acid tertiary sand and is comprised of dense

Fig. 1. Map of the City of Cape Town showing the extent of remaining Cape FlatsSand Fynbos in relation to urban areas. Crossed boxes indicate study sites. (Derivedfrom the City of Cape Town vegetation map and biodiversity network map,Appendix S1.)

S.G. Dures, G.S. Cumming / Biological Conservation 143 (2010) 768–777 769


ericoid-leaved shrubland, the structural composition of the com-munity being dominated by proteoid and restioid fynbos. The veg-etation type is species rich and contains numerous endemicspecies (Holmes et al., 2008).

Since the 1960s, Cape Town’s urban planners have favouredlow-density housing developments, leading to the proliferation ofurban sprawl and altered land use. According to the City of CapeTown’s Biodiversity Report, land use change is especially prolificin the Cape lowlands where only small areas of vegetation are for-mally conserved, resulting in fragmentation of the natural biotainto small islands, isolated both from each other and from sourcepools of native fauna and flora (Holmes et al., 2008). Cape FlatsSand Fynbos has also been heavily invaded by the Australian Acaciasaligna (Port Jackson Wattle). The massive invasion of the lowlandsof the fynbos biome by A. saligna, with little or no evidence of pre-dation, has been aided by two characteristics that enable it to out-compete the native flora. First, it is adapted to a Mediterraneanclimate and nutrient poor soils. Second, it produces about 10,000seeds per 1 m2 of canopy cover per annum, the dispersal of whichare aided largely by birds (Richardson et al., 2000; Witkowski,1991). Once established this tree forms tall dense stands that, fol-lowing fire, regenerate quickly due to the propensity for the spe-cies to coppice readily (Milton and Hall, 1981), shading out theindigenous flora and altering the soil composition by dramaticallyincreasing the soil nitrogen load in soil otherwise characterised bylow nutrients (Yelenik et al., 2004).

2.2. Field methods

Bird point counts were undertaken at the centre of each studysite in the middle of the Cape summer (i.e., November–January,which, according to the Roberts birds of South Africa database(Hockey et al., 2005), coincides with breeding periods for a largeproportion of the species likely to be encountered). Each countlasted for an hour, starting 30 min after sunrise, on clear days whenwind was mild to moderate. Counts were preceded by a 10-minhabituation period. We recorded species present and the numberof individuals within a 50 m radius. Only birds actively using thesite were recorded. Counts included birds such as raptors that werecircling over the site, possibly using it as a hunting ground. Birdsflying over the site without pause were not counted. Samplingwas not undertaken during rain, thick mist or high winds. To elim-inate the possibility that seasonal changes in species compositionoccurred through the sampling period, the first 15 sites visitedwere re-sampled at the end of the data collection period. Samplingeffort was kept constant and consistent species composition ob-served during return visits suggested the observation period tobe sufficient. The differences in detectability of each species werenot accounted for as we were not concerned with comparisons be-tween individual species, but between sites. It was assumed thatdetectability of a species remained constant between study sites.Each bird was classified into one or more of nine functional groupsbased on the classification scheme of Cumming and Child (inpress), which was in turn based on life history and ecological datafrom the compiled Robert’s VII bird species database (Hockey et al.,2005).

The location of each point count was recorded using a GarminGPSMAP 76S GPS unit. At each site we also measured the averagecanopy height of the dominant vegetation and the density of A. sal-igna within a 20 m quadrat. Average canopy height was measuredby taking the average height of the vegetation type covering thegreatest percentage of the ground within a 20 m quadrat, acaciadensity was measured by randomly selecting 10 acacia stems atdistances greater than 5 m apart and taking the average distanceto the nearest neighbour to obtain an estimate of overall density.

2.3. Broad-scale landscape measures

Remotely sensed data were used to calculate a number ofbroad-scale landscape measures relating to each study site. All re-mote spatial analysis was performed in Arcview 3.3 (ESRI, 2002)unless otherwise specified. All data and map layers are referencedin Appendix S1.

The proximity of each point count to Table Mountain was mea-sured from a 1:50,000 scale orthographic map. Proximity to wet-lands and the distance from dense urban areas were calculatedfrom maps provided by the City of Cape Town. To calculate a mea-sure of urban density the numbers of buildings within circularareas with radii of 250 and 2500 m from the point count locationwere counted from aerial photographs with a resolution of45 cm. This method assumed that the number of buildings wouldact as an approximation for the total amount of built on land. Aver-age normalised difference vegetation index (NDVI) was calculatedat the same scales (250 and 2500 m). NDVI can be used as an indi-cator of net primary productivity and is useful for detecting areasof high and low productivity (He et al., 2009); we used a Landsat7 TM data set with a resolution of 30 m. There is a lack of evidencefor a growing season in the Fynbos biome (Pierce, 1984) and sincewe were primarily concerned with the effects of fragmentation andchanges to the composition of fynbos, and not that of the non-na-tive vegetation, an image taken during the winter month of Julycould be used in the hope that the influence of acacia growthwould be reduced. Furthermore, the highest quality Landsat imageavailable could be selected.

To quantify connectivity, defined as an organism’s ability tomove through a spatially structured landscape (Moilanen andNieminen, 2002), the distance to the nearest neighbouring patchwas calculated in Fragstats (McGarigal, 2000). Because of the po-tential weaknesses of nearest neighbour measures (Moilanen andNieminen, 2002), we also used an Incidence Function Model(IMF), described by Hanski (1994):

Si ! R exp"#adij$Aj

where Si is a measure of connectivity for patch ‘i’ to other potentialsource populations ‘j’ and a scales the effect of distance to migra-tion, where 1/a is the average migration distance, dij is the distancefrom path ‘i’ to patch ‘j’, and Aj is the size of the potential source site.To calculate dij, the ArcView extension ‘Distance Matrix’ (Jenness,2005) was used to create a site-to-site matrix. Because this measureof connectivity is based on the movement ability of individuals(Franken and Hik, 2004) and we were interested in a group of spe-cies using the landscape at different scales, a measure of connectiv-ity was taken using a number of different values for a. Bowman(2003) demonstrated that avian dispersal distance is proportionalto the square root of territory size by a multiple of 12 and thatthe relationship is especially significant among non-migrants. Wescreened Roberts birds of South Africa (Hockey et al., 2005) forinformation regarding territory size or dispersal distance for allbirds found during data collection and based on this summary, re-corded connectivity scores based on dispersal distances of 0.125,1.5, 5 and 10 km. For distances greater than 10 km degree of con-nectivity was not found to increase significantly. In general, mostrecorded birds for which data was available had territory sizes ofless than 1 hectare, although there can be considerable variationin territory size and the Roberts’ database is not complete for allspecies.

An important component in any fragmented landscape is thepermeability of the matrix. To account for the possibility that birdswill move through vegetation that is similar to (but not defined as)Cape Flats Sand Fynbos, each site’s connectivity was also calculatedusing NDVI, rather than the anthropogenic definition of each

770 S.G. Dures, G.S. Cumming / Biological Conservation 143 (2010) 768–777


patch’s boundary. An NDVI image allocates each pixel of a land-scape image a value. For our study site these values were dividedinto eight groups each with the same variance of NDVI scores(again using ArcView 3.3). Each of the eight categories was visuallycompared with the distribution of known Cape Flats Sand Fynbosusing orthorectified aerial photography and only those groupsmost closely matching the fynbos distribution (in this instancethose values falling into category 8) were used for the connectivitycalculation. The connectivity the landscape falling into just NDVIcategory eight was then calculated at the four chosen focal scalesof bird dispersal. The resulting output was a connectivity scorefor each pixel found in NDVI category 8. The average connectivityscore of all pixels falling within each of the focal sites was then cal-culated to give a connectivity value for each study site.

2.4. Fine scale landscape measures

A number of fine scale landscape measures relating to eachstudy site were also calculated. The total area and the perimeterof each sites was calculated in Arcview 3.3 (ESRI, 2002). After con-verting to raster data, Fragstats (McGarigal, 2000) was used to cal-culate shape index, a similar metric to the perimeter-area ratio butstandardised to a simple Euclidean shape. Without this standardi-sation, holding the shape constant and increasing the size will re-sult in a decrease in the perimeter-area ratio (McGarigal, 2000).

2.5. Site selection

GIS analysis of the City of Cape Town’s current vegetation cov-erage map and biodiversity network map produced a possible sam-ple size of 416 remaining Cape Flats Sand Fynbos sites. Fragmentswere identified as individual sites if they were geographically iso-lated or if they were isolated from patches of the same quality cat-egory, as identified in the Cape Town biodiversity network map.Site selection was undertaken using the methods outlined in Spies-man and Cumming (2008). K-means cluster analysis in SPSS (SPSSInc., 2007) was used to assemble the pool of potential sites into 50relatively homogenous groups that represent the full range of het-erogeneity across the study region. We determined groups usingpatch size, perimeter, shape, distance from dense urban areas,proximity to fresh water, shape and nearest neighbour distance.The IMF connectivity measure was not used, as much of the re-quired data had not yet been collected, and so a similar patch prox-imity measure was calculated using Fragstats 3.3 (McGarigal,2000). This technique assigns each site to a cluster based uponoptimising of the chosen characteristics, where the number of clus-ters is predetermined (50). The groups will be non-nested and willrepresent the full heterogeneity of the chosen variables (McCuneand Grace, 2002).

From this group of 50 clusters a single representative site fromeach cluster, namely a site that was feasible to work in and fell asclose to the mathematical centre of the cluster as possible, waschosen for sampling. The physical centres of all sites were sepa-rated by at least 1 km, occurred within separate patches andspanned a range of approximately 1500 km2, covering most ofthe City of Cape Town. Due to difficulties with access and security,the final sample size was 39 sites (Fig. 1).

2.6. Statistical methods

Statistical analyses were performed using SPSS (SPSS, 2007). Alldata were tested to check their approximation to normality, usingboth the Shapiro–Wilks and the Kolmogorov–Smirnoff tests fornormality. Logarithmic, inverse or square root transformationswere made where necessary. The measure of urban density wasnot normally distributed, either in its raw state or following trans-

formation, but showed a bivariate ‘dust-bunny’ distribution withmany points lying close to one of the two axes (McCune and Grace,2002). To enable the data to approximate the normal distributionfor use in parametric tests, seven of the sites, each containing lessthan 100 buildings within a 2.5 km radius, were randomly selectedfor removal from the analysis, thus removing the bimodal distribu-tion caused by an accidental sampling bias for low urban densitysites. Wherever a statistical test was dependent on the dataapproximating the normal distribution this reduced data set wasused. Urban density at the 250 m spatial scale could not be madeto approximate normality and had to be excluded from all para-metric tests.

2.7. Dependent variables

Taxonomic richness is not always a good predictor of functionalrichness (Cumming and Child, 2009). This observation may haveimportant ecological ramifications for interpreting increases intaxonomic richness under moderate urbanisation (McKinney,2008). We therefore considered two main dependent variables,species richness and functional richness. Functional richness wascalculated using the nine functional groups identified by Cummingand Child (2009), taking into account the fact that individual, moregeneralist species may belong to one or more functional groups.We also ran all statistical tests independently for each functionalgroup.

2.8. Bivariate analysis

Both urban density and acacia density could be divided intoroughly equal sized categories, urban–rural and acacia presence–absence; each one with multiple samples. A test for analysis of var-iance (ANOVA) was calculated between the two categories, withrespect to the dependent variables. The F statistic of the ANOVAmodel is relatively robust to deviations from normality (Glass etal., 1972) thus allowing the test for the influence of urban densityto be performed on the non-reduced data set. The divide betweenurban and rural sites was considered at two intervals of buildingdensity, P1000 buildings per 2.5 km radius and P2000 buildingsper 2.5 km. Sites with acacia present or absent divided the acaciavariable.

To identify correlations occurring both between the dependentand independent variables, as well as intercorrelation amongst theindependent variables, we performed non-parametric Spearman’srank correlations and partial correlation analysis. Correlation testswere also performed on the dependent variables, using both theSimpson’s Diversity Index (D) (Simpson, 1949), to account for var-iation in species populations between sites, and a Jack-knife esti-mate of species richness (S) (Heltshe and Forrester, 1983) toaccount for the probability of missing some of the actual totalnumber of a species present. Neither of these statistics displayedobvious deviations from the species richness correlations, meaningthat the occurrence data for individual species within each site didnot significantly alter the outcome of the tests. Because of the dif-ficulty in controlling for deteriorating visibility in areas of high A.saligna density, potential error due to recounting the same individ-ual multiple times in thick vegetation, and the lack of influence ofthe count data we decided that only presence–absence data (ratherthan abundance data) would be used.

To test for the influence of species rarity and specialist or gen-eralist birds, Spearman’s rank correlations were again used. Thedependent variable was replaced with the number of birds fallingeither into just one or into multiple functional groups and thenwith the number of bird species with only a single count withina site. Using these dependent variables produced no significantdeviations from the other reported response variables.



2.9. Path analysis

The data were highly intercorrelated and many variables werespatially structured. Partial correlation analysis was used to correctfor some of the effects of the interaction between variables, high-lighting a smaller set of important variables (urban density, connec-tivity, density of invasive acacia andaverage canopyheight) that stillcorrelatedwith community composition. To further explore interac-tions within the system we developed a simple causal model thatcaptured the main elements of the problem, displaying the interac-tions and driving variables expected to be driving the patterns ob-served with the community composition. This model is a diagramdeveloped a priori to be analyzed using path analysis. Path analysisis essentially an application ofmultiple regressions, based on stand-ardised variables, in which the order of the regressions is specified apriori by a path diagram (Shipley, 2002). It uses partial regressions,guided by insights from graph theory, to estimate the likely maxi-mum strengths of the interactions between variables, while stillcompensating for other relationships within the model. In fact itcan be used to quantify the effects of both direct interactions andindirect interactions (i.e. those acting via an intermediate variable;e.g., Cumming, 2004). It must be emphasized that path analysis isa method to link causation and correlation (Shipley, 2002) and isdependent on the judicious and careful a priori selection of a modelbased upon ecological understanding. By keeping the model simplewe hoped to exclude erroneous assumptions of causality.

Following standardisation of all the data (by subtracting by themean and dividing by the standard deviation for each variable),path analysis was performed using AMOS 5 (Arbuckle, 2003). Thisinvolves incorporating the smaller number of independent vari-ables identified as being correlated with community compositioninto the model developed a priori, along with the dependent vari-able for which the model is being run.

To account for the possible significance of spatial autocorrela-tions, we followed the procedure of Borcard et al. (2004) and devel-oped a set of interaction terms up to the third-order using the xand y coordinates of each of our study sites (e.g., x, y, x2, y2, x3, y3, x-y, xy2, x2y, etc. . ., up to x3y3). Following a Spearman’s rank correla-tion, each of the interaction terms showing significant correlationwith the path analysis variables were incorporated into a PrincipalComponent Analysis (PCA) to reduce the data into a smaller num-ber of uncorrelated variables (Pearson, 1901). The derived compo-nents which explained most of the variance were thenincorporated in the path analysis model, accounting for the vari-ance due to spatial autocorrelation (for further details see Borcardet al., 2004).

The model was analyzed using variables calculated at multiplescales to consider both changes in the scale of connectivity as wellas the radius at which urban density was assessed. Each individualfunctional group was also considered independently as the re-sponse variable in the path model.

3. Results

We recorded 79 bird species across 39 Cape Flats Sand Fynbossites. No species was found at every site. The most commonlyoccurring species were Cape White Eye (Zosterops virens), KarooPrinia (Prinia maculosa), Cape Turtle Dove (Streptopelia capicola),and Pied Crow (Corvus albus). Each was found on more than halfof the sites. For a full bird list see Appendix S3.

3.1. Bivariate analysis

Analysis of variance between sites with and without acacia re-sulted in a highly significant difference in both species and func-

tional richness (F = 15.323, n = 39, p 6 0.001 and F = 14.894,n = 39, p 6 0.001 respectively; Brown–Forsythe test for equalityof variances p 6 0.001; Brown and Forsythe, 1974). Similarly anal-ysis of variance of species and functional richness between urbanand rural sites showed significant differences when the urban ruraldivide was taken at P1000 buildings per 2.5 km radius (F = 8.656,n = 39, p 6 0.01 and F = 8.589, p 6 0.01 respectively; Brown–Forsy-the test for equality of variances p 6 0.05) or P2000 buildings per2.5 km radius (F = 6.691, n = 39, p 6 0.05 and F = 6.463, p 6 0.05respectively; Brown–Forsythe test for equality of variancesp 6 0.05; Brown and Forsythe, 1974).

Eight of the initial 19 independent variables showed significantcorrelations (p 6 0.05) with species richness as well as with func-tional richness, using Spearman’s rank correlations (AppendixS2). However, there was also significant intercorrelation betweenindependent variables at multiple scales and permutations. Mostnotable of these were connectivity, urban density, distance to wet-lands, distance to Table Mountain National Park, acacia density andcanopy height. Surprisingly, no significant correlations were ob-served between any of the dependent variables and the local patchmetrics, even at a significance of p 6 0.1. There were no significantcorrelations between the patch metrics and any other independentvariables to control for.

To correct for interaction effects, we performed partial correla-tion analysis on connectivity, acacia density, distance to wetlands,canopy height and urban density within 2.5 km (Table 1). Whenusing urban density within a radius of 2.5 km and controlling forall significant correlations, the significance of the urban gradienton species and functional diversity was reduced but still remainedsignificant at the p 6 0.05 and p 6 0.1 levels respectively. Thestrength of the correlation was also slightly reduced. The otherindependent variable that retained a significant correlation withthe two dependent variables was connectivity, measured using adispersal distance of 10 km. By comparison, the other three vari-ables lost all statistical significance in their correlations with thedependent variables once intercorrelation was accounted for.

3.2. Path analysis

Before finalising the model we tested the effect of the two xyfactors generated to compensate for spatial autocorrelation. We re-placed the highly autocorrelated broad scale factors of urban den-sity and connectivity by measurements made at a much finer scale(250 and 125 m respectively), and reran the model. These fine scalemeasures display far less spatial autocorrelation. By changing thescale of observation, the previous high significance of the xy factorsbecame highly insignificant. This suggests that incorporating the xyfactors overly compensated for the two driving variables, urbandensity and connectivity, because of their inherent spatial autocor-relation. We therefore excluded the autocorrelation-compensatingvalues. The lack of correlation with the Y variable (r = #049,p = 0.001) also indicates a negative correlation of diversity withincreasing latitude helping to confirm any influence of AtlantisSand Fynbos, found to the north of the study sites, acting as asource population for the fragmented Cape Flats Sand Fynbos.

It is also notable that in the finer scale model the urban densityvariable showed no significance, justifying the use of urban densityonly at the wider 2.5 km scale in the final model. The model wasrun with all four measures of connectivity, as well as with a singlePCA factor that described 88.59% of the variance seen among thefour variables. For the PCA factor a KMO and a Bartlett’s test forsphericity were performed, confirming that the correlations amongthe variables were small enough and weak enough for a factoranalysis to be valid (KMO = 0.521, Bartlett’s 6 0.001, n = 39). Foreach connectivity variable the model gave only subtle changes insignificance, however, it was decided to retain the PCA factor for



connectivity instead of the single factors as this resulted in thelowest chi-square value for the entire model.

The final path model, using species richness as the responsevariable (Fig. 2), had a very low chi-square with a low probabilitythat any departure of the data from the model predictions was sig-nificant (0.008, df = 1, p 6 0.927). All goodness of fit indices calcu-lated were highly significant (GFI = 1, AGFI = 0.999) suggesting thatthe model was a faithful predictor of the data, and both the

Akaike’s information criterion (AIC) and the Bayes information cri-terion (BIC) were lower in the constructed model than in one gen-erated without constraints. This suggests the rules imposed on themodel improved its fit to the data (Akaike, 1974; Schwarz, 1978).

The results of the model suggest that it is adequate for the pur-pose intended; to assess the relative importance of variables iden-tified by bivariate analysis to be significant but displayingcovariance. The strength of the error value associated with thedependent output variable, species richness, suggests that addi-tional parameters would be required to fully model the completesystem; however, these are unlikely to drive the patterns observed.

We can infer from the model that there are three major driversof species richness in this system; Canopy height (path coeffi-cient = #1.88, p 6 0.001), urban density at a broad scale (path coef-ficient = 1.33, p = 0.023) and local acacia density (pathcoefficient = #1.25, p = 0.027). This outcome makes good intuitivesense as each of these three factors could dominate a landscapeby affecting structure, nutrients, floral diversity and niche diver-sity; both within the patch and across the intervening matrix.We can also confirm that the mechanisms behind the model de-scribe the patterns of species richness observed without usingany local patch metrics, most notably area. Field observations sug-gest that acacia density is driving a dramatic and obvious change inthe dominant canopy height and correlation analysis demon-strated a very close relationship (rs = 0.661, p 6 0.001). It is unfor-tunate that the relationship between acacia density and canopyheight appears to be bimodal, as this precludes its accurate inclu-sion in the model. This anomaly is possibly caused by the rapid anddramatic difference in canopy height in those areas infested withacacia. The exclusion of this link should not affect the overall out-come of the model as it is a secondary path to species richness, solong as the reader is aware of its presence.

To assess the ability of the model to predict the responses ofeach functional group to changes in the driving variables, the ‘Spe-cies richness’ variable in the model was replaced by the species

Table 1Partial correlation of all variables significantly correlated with Species and Functional richness, controlling for intercorrelated variables.

Control variables Species richness Functional richness

Distance to wetlands Connectivity @ 10 km Correlation #0.196 #0.288Distance to urban areas Significance 0.317 0.138Urban density 2 2.5 km df 26 26Acacia density

Acacia density Distance to urban areas Correlation #0.210 #0.178Urban density @ 2.5 km Significance 0.283 0.364Canopy height df 26 26Distance to wetlands

Urban density Distance to urban areas Correlation 0.403 0.314@ 2.5 km Acacia density Significance 0.034 0.104

Distance to wetlands df 26 26Canopy heightDistance to Table Mountain

Canopy height Connectivity @ 10 km Correlation #0.122 #0.146Connectivity @ 5 km Significance 0.529 0.448Acacia density df 27 27

Connectivity Connectivity @ 0.125 km Correlation 0.479 0.448@ 10 km Connectivity @ 1.5 km Significance 0.011 0.019

NDVI @ 2.5 km df 25 25Canopy heightDistance to wetlands

@ 5 km Connectivity @ 0.125 km Correlation 0.324 0.260Connectivity @ 1.5 km Significance 0.100 0.191NDVI @ 2.5 km df 25 25Canopy heightDistance to wetlands

Fig. 2. Path analysis model. Values represent strength of direct effects estimatedusing maximum likelihood with p-values below. ‘E’ indicates unspecified measure-ment errors estimated during model calculation. Broken path line represents pathexcluded due to bimodal distribution.



richness by patch of each functional group in turn (Table 2). In allcases the tests for model fit mirrored those with species richness asthe response variable, indicating that the model retained its origi-nal integrity. For each group there appeared to be clear differencesin the driving variables although in general they followed the samepatterns as that of the species richness model. Although both scav-engers and nutrient dispersers showed a significant negative trendtowards connectivity, possibly indicating functional replacementwithin the more urban areas, the majority of the path connectionsshowed little significance and we would advocate not over inter-preting model results.

4. Discussion

Changes in bird diversity across an urban gradient are clearlyinfluenced by many variables. However, it appears in this contextthat urban density, the density of the invasive A. saligna and vege-tation structure are the greatest influences on community compo-sition. Interestingly, we found no correlation between speciesrichness and any of the local patch metrics, nor between patch sizeand species or functional diversity. In this context the species areacurve does not fit; it would appear there are more important influ-ences on community composition that need to be considered. Ourresults highlights the relative redundancy of local patch metrics inpredicting the implications of fragmentation in a situation wherethere may be confounding factors affecting the quality of a frag-ment. Patch isolation or connectivity also seem to play a relativelyinsignificant role. While connectivity appears to influence acacia,and thus indirectly richness, in general it is not driving the ob-served patterns. This is supported by the recent review by Prughet al. (2008) who could attribute only 13 and 3% of the deviationin patch occupancy to area and isolation respectively.

Across Cape Flats Sand Fynbos habitat there is a significant in-crease in the number of bird species observed as the number ofhouses found within a broad radius increases and the local densityof acacia decreases. The pattern observed with respect to acacia isnot surprising given that it forms thick monocultures. In a 1970study Winterbottom found that acacia stands held approximatelyhalf the number of species as in the Sand Fynbos. At the landscapelevel, community dynamics are driven by changes in the extentand distribution of suitable habitat, both with regard to size andto quality (Siriami et al., 2008). As acacia blocks out sunlight fromthe scrubbier fynbos below and changes the soil nitrogen composi-tion, making it less favourable to the native species (Yelenik et al.,2004), the suitability of the habitat will be reduced. Differences inhabitat selection mechanisms, territory requirements, require-ments for structural complexity and specialist niche openingsmay all play a role in determining a species presence or absencefrom a habitat fragment, but this will become irrelevant withouthabitat heterogeneity. Theory would thus predict that monocul-ture should support less biodiversity (Hector et al., 1999). Further-more, the acacia grows far quicker than the native fynbos followingfire (Milton and Hall, 1981) especially when due to coppicing and

the canopy height variable thus describes acacia height. The resultis a strong negative correlation of bird species richness with can-opy height.

The influence of urban density was less expected, despite simi-lar results being documented by other studies (Leveau and Leveau,2004; Filippi-Codaccioni et al., 2008; McKinney, 2008). Althoughsome studies dispute the fact, birds are generally thought to de-crease in diversity with increasing urbanisation through a closenegative correlation with vegetative complexity and plant speciesrichness (McKinney, 2008). Our study would on first sight appearto contradict this pattern. It is also notable that there is a negativecorrelation between urban density and acacia density. There areseveral reasons why this may be the case. The close scrutinisationafforded to remnants of a largely urban, threatened habitat is likelyto create stronger management incentives within the city than onits outskirts. Fire is also more heavily controlled within a city andA. saligna requires fire to germinate (Yelenik et al., 2004). Com-bined with rapid growth rates, this results in dense stands of theacacia outside urban areas where fire may persist. The dominantmonoculture is clearly reducing bird species diversity outside thecity but not within it. An urban or peri-urban environment pro-vides considerable heterogeneity in structure, plant diversity andconnectivity; through gardens, scrublands, empty buildings andremnant native fragments. The presence of a homogenising inva-sive species is confounding the relative value of the urban environ-ment to provide a refuge for the native flora and fauna. Bydegrading the quality of Cape Flats Sand Fynbos habitats outsidethe dense urban areas, where open space is poorly managed, thespecies and functional richness observed within the city becomesoveremphasised, because of an increase in the habitat quality forthe birds relative to the areas infested with acacia.

The functional analysis produced no surprises. In general, themajority of functional groups followed a trend similar to speciesrichness. This is to be expected and simply confirms that loss ofspecies will result in a reduction in functional diversity. It is clearfrom this study and others that in the urban context there is con-siderable homogenisation of biodiversity, and there is no reason tosuspect this should not be the case within the avifauna of CapeTown as has been seen in other cities (Clergeau et al., 2006). Thishomogenisation may explain the lack of clear functional shifts be-tween the urban and rural study sites as generalist alien speciesbegin to replace the functions of the native species. For example,crows seem to be replacing the raptor functions usually filled bybuzzards or kites. However, generalisations about the effects ofalien birds from our data are inappropriate; in-depth analysis ofthis question would require more detail. This study is using acoarse approach to functional classification so although one spe-cies may appear to be ‘replaced’ by another there is no expectationthat the functions will be identical. Non-native bird species could,however, contribute further confounding variance to relationshipsbetween bird communities and habitat configuration and area.

Bivariate analysis demonstrated that there was a significantchange in species richness with respect to both urban density

Table 2Path correlations for eight of the functional groups. Significant path values in bold.

Group Urban density Connectivity Canopy height Acacia density

path p path p path p path p

Seed eaters #0.033 0.838 0.109 0.488 #0.384 0.018 #0.134 0.388Pollinators 0.337 0.025 #0.132 0.385 #0.199 0.197 #0.266 0.072Nutrient dispersers 0.338 0.015 #0.275 0.04 #0.32 0.022 #0.196 0.151Insectivores 0.253 0.07 0.085 0.533 #0.402 0.004 #0.197 0.143Granivores 0.149 0.329 0.091 0.543 #0.286 0.064 #0.28 0.058Grazers #0.301 0.051 0.316 0.032 0.155 0.311 #0.034 0.821Scavengers 0.185 0.186 #0.46 0.000 0.027 0.843 #0.211 0.135Raptors 0.039 0.826 #0.13 0.445 0.008 0.961 #0.133 0.454



and acacia. By looking at the correlations and intercorrelations act-ing within the landscape, the path model clarifies the strength andsignificance of the influence of urban density and the invasive alienA. saligna. The irrelevance of habitat amount was demonstrated bythe absence of any correlation between species richness and patchmetrics. The path model demonstrated the relative redundancy ofconnectivity. Both habitat amount and arrangement are overshad-owed by confounding variables in fragmented urban landscapes.

4.1. Further considerations

Most conservation biologists focus on the retention of largepatches of undisturbed habitat as reserves, with intact corridorsproviding the connectivity and that reserves are the only way toconserve biodiversity (Franklin and Lindenmayer, 2009). By con-trast, we found that in an urban setting the size and shape of rem-nant patches of intact vegetation may be less important than theirquality.

The degree to which the quality of ‘‘unsuitable” habitat affectsdispersal ability is poorly understood. In an urban context the het-erogeneous nature of both the structure and vegetative composi-tion of the landscape create additional complexities. Privategardens, for example, may provide valuable connections for birdsif planted in a favourable way, or (if paved or occupied by a domes-tic cat) may be entirely unsuitable. In an urban environment theremay be numerous islands of suitable habitat that can act as step-ping-stones, or even as permanent habitat, allowing greater levelsof connectivity or suitability than are immediately obvious. Thecomposition of the matrix surrounding a patch within a frag-mented urban landscape has a large influence on the compositionof its bird community. Until recently this has been overlooked bymany conservation biologists (Krauss et al., 2003; Prugh et al.,2008; Franklin and Lindenmayer, 2009). Recent studies have foundspecies responses to fragmentation to be profoundly influenced bythe matrix type (Antongiovanni and Metzger, 2005; Ewers andDidham, 2006) and a recent review of 1015 population networksfound that while patch isolation and area were poor indicators ofspecies occupancy, the nature of the matrix was a consistentlystrong predictor (Prugh et al., 2008).

In addition to the spatial aspects of fragmentation, the temporalaspect of relaxation must be considered (Gonzalez, 2000). Urbanareas are not discreet, unchanging entities but fluid elements of alandscape, the characteristics of which change through both timeand space. A high quality patch may rapidly decline in its abilityto support a diverse community due to invasion by alien speciesor anthropogenic habitat modification. In this case all metrics per-taining to isolation, relaxation and the permeability of the inter-vening matrix change within a year or two; the complexity ofthe problem further increases if the multiple biotic componentsof the patch are taken into consideration. The rapid growth ratesand invasive ability of A. saligna can cause such rapid changes inthe landscape that any effects of relaxation due to fragmentationwill be overshadowed.

4.2. Time and scale

Spatial and temporal scale are critical in interpreting the resultsof this study. There is often an assumption that once the mecha-nisms that describe species richness have been found they can bescaled up or down to suit the needs of further applications. Biolog-ical systems are often more complex than this (Willis and Whittak-er, 2002). This study was conducted at a local scale, focusing onspecies richness within communities and between habitat patchesof a similar type. At a coarser spatial scale or across patches of hab-itat of a different type, there is a much lower likelihood that urban-isation or land use homoginisation will play such a significant role.

It is also notable that the relative increase in the diversity observedwithin the more urban patches of this study is related to the de-cline in diversity seen in the areas influenced by homogenisingvegetation. If larger patches of intact native vegetation were pres-ent, or were studied over a larger extent, we might find that thepatterns we observed would become less significant.

The temporal scale of the study may also impose some limita-tions on the conclusions of the study, since some of the influenceson species richness observed could be a function of time sinceinvasion or urbanisation (Fairbanks, 2003). Relaxation effects couldstill be in play, as discussed earlier, and recolonisation from regio-nal source pools might occur in the future. Considering the limitedextent of the vegetation type studied and the fact that few standscan be considered likely to reach any kind of equilibrium, however,regional processes can not necessarily be relied onto restore localavian diversity. In all areas where the acacia is present in densestands there is a significant drop in bird diversity, and in this in-stance its homogenising influence is producing an overriding signalthat is unlikely to be ‘overcome’ over time without direct humanmanagement.

For the local fauna and flora of the Cape Flats Sand Fynbos thereis some hope. Advances in biocontrol in the past couple of decadeshave resulted in a highly effective gall-forming rust fungus,Uromycladium tepperianum. In sites where it has been introducedor subsequently infected there has been a reduction in tree densityof between 87 and 98%, with an annual tree mortality rate of 18%(Wood and Morris, 2007). Hopefully A. saligna is being brought un-der complete biological control and, if combined with properlymanaged clearing operations, the damage done by this invasiveweed may be reversed (Morris, 1999; Wood and Morris, 2007).Nevertheless, in the current landscape it is one of the dominantinfluences on local bird community composition.

5. Conclusions

This study sheds light on the nature and complexity of fragmen-tation in an urban and peri-urban environment. Specifically, ithighlights that urban conservation biologists and urban biodiver-sity planners need to focus their efforts away from the size, shapeand location of remnant intact areas of biodiversity, and instead fo-cus on identifying those factors of the urban landscape that allowthe persistence of species within a diverse matrix. It further high-lights a pressing need to ensure that the quality of habitat is mon-itored and that urgent action be taken to tackle homogenisinginfluences such as invasive alien species. Above all, attention mustbe focused on the nature of an urbanising landscape and its useful-ness to different species. This work underscores the need for pat-tern-based studies to be replaced with more mechanisticapproaches to conservation biology (see also Lindenmayer andFischer, 2006; Cumming, 2007). If we are to understand how anever-growing urban environment can support the persistence oflocal biodiversity we must first ensure that we understand themany factors and processes at work within the fragmented urbanlandscape.

Acknowledgements

We gratefully thank the many people who helped us locate GISdata and offered useful advice during the planning stages of thisproject, particularly H. Ernstson, N. Allsopp, P. Holmes, C. Dorse,A. Stripinovich, N. Lindenburg, M. Burgher, K. Tolley, J. Measey,M. Mills, T. Hoffman, M. Vogel, J. Battersby-Lennard, T. Slingsby,C. Haskins, K. Sinclair-Smith and J. Gie. Field work was supportedby the University of Cape Town through a URC Grant to GC and aPPI bursary to SD.



Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.biocon.2009.12.019.

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