The role of urban schools in biodiversity conservation across an urban landscape

Urban Forestry & Urban Greening 43 (2019) 126370

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The role of urban schools in biodiversity conservation across an urban landscape

T

Justice Muvengwia,b, , Anesu Kwendab, Monicah Mbibac,b, Tapiwanashe Mpindub ⁎

a

School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Private Bag 3, Johannesburg 2050, South Africa Department of Natural Resources, Bindura University of Science Education, Private Bag, 1020 Bindura, Zimbabwe c Sustainability Research Unit, Nelson Mandela University, George Campus, P/Bag X6531, George 6530, South Africa b

ARTICLE INFO

ABSTRACT

Handling Editor: W. Wendy McWilliam

Previous studies have clearly outlined the importance of urban green spaces such as golf courses, urban gardens and street sides in urban ecosystem functioning. To date little has been done to consider the role of urban school yards in biodiversity conservation in Southern Africa. Our study therefore investigated the role of urban school yards in biodiversity conservation across an urban gradient in the city of Harare, Zimbabwe. Ten schools were randomly selected from each stratum, low, medium and high density suburbs. School yards were surveyed for herbs (garden flowers) and woody plants (trees and shrubs). For α diversity, species richness, Shannon and Simpson diversity indices were computed using the Hill numbers. Variation in species composition among schools was assessed using beta diversity (βSOR) and its components (βSIM and βSNE). A total of 120 tree species belonging to 43 families and 89 garden flowers belonging to 41 families were identified and recorded in school yards from the different suburb densities. Schools in medium density suburbs had highest species richness, Shannon, Simpson and beta diversity indices for indigenous trees compared with those in the low and high density suburbs. For garden flowers, there was no variation in species richness, Shannon, Simpson and beta indices across school yards from low, medium and high density suburbs. Our results demonstrate the important role of urban school yards in biodiversity conservation in general, though there is species homogenization of garden flowers and exotic trees across the urban landscape.

Keywords: Biodiversity Beta diversity Hill numbers Shannon diversity Simpson diversity Urban schools

1. Introduction The loss of biodiversity has become an issue of global concern (Hui, 2013). Biodiversity loss has been aggravated by the ever increasing human population and demand for more land (Groom, 2006). Vast pieces of land are being cleared in a bid to provide space for infrastructural development, mining and agriculture (Slingenberg et al., 2009). The world’s human population is projected to reach 8.6 billion by 2030 (United Nations Department of Economic and Social Affairs, 2017), thus increasing the demand for resources from ecosystems and space for housing developments. The increase in number of people living in cities has principally resulted in the physical growth of urban areas (Mosammam et al., 2017). As urban population increases, the demand for land to build houses, industries, sporting facilities, roads and schools also increases resulting in the removal of existing vegetation (Jim, 2000). This rapid expansion of infrastructural development has negative effects on different ecosystem services (Grimm et al., 2008; Walker et al., 2009). For

⁎

instance, provisioning services, regulating services, cultural services and supporting services (Costanza et al., 1997; Morgenroth et al., 2016) Efforts have been made to protect species within their natural habitats, for example national parks, sanctuaries and conservancies as well as out of their natural areas of existence, for example gene banks and botanical gardens (Borokini, 2013). These conservation measures have played a significant role in the preservation of wild species, genetic resources and in reducing human activities within protected areas where endemic and rare species are often found (Rossi et al., 2014). In cities, town planners have resorted to the use of green spaces, golf courses and street sides to preserve urban biodiversity (Lepczyk et al., 2017; Threlfall et al., 2017). These areas have helped to conserve biodiversity and to maintain ecosystem processes in such landscapes (Aronson et al., 2017; Lepczyk et al., 2017). Residential areas of most African cities are structured into low, medium and high density suburbs in relation to wealth and human population density per unit area (Wania et al., 2014). Studies have previously shown that in cities, household income or wealth is related

Corresponding author at: School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Private Bag 3, Johannesburg 2050, South Africa. E-mail address: [email protected] (J. Muvengwi).

https://doi.org/10.1016/j.ufug.2019.126370 Received 12 August 2018; Received in revised form 3 June 2019; Accepted 14 June 2019 Available online 17 June 2019 1618-8667/ © 2019 Elsevier GmbH. All rights reserved.

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Fig. 1. Location of sampled schools in the low, medium and high density suburbs of Harare, Zimbabwe.

Table 1 Area of school yards across three sites, low, medium and high density suburbs in Harare expressed as mean ± SE. Location

Area (ha)

Low density suburb Medium density suburb High density suburb

7.694 ± 1.216ab 10.216 ± 2.071b 4.632 ± 0.556a

This phenomenon highly fits in the ‘human ecosystem model’ which links the state of urban environments to socio-economic status. We presume that the level of biodiversity is highly related to urban density gradient. Although studies have been carried out on the role of green spaces (Lepczyk et al., 2017), backyards or golf courses (Watson and Eyzaguirre, 2002) on urban biodiversity, little is known on the potential role of urban schools occurring across an urban density gradient in biodiversity conservation. In fact, from available literature little has been done to explore the contribution of school yards in an urban environment to vegetation diversity conservation. Pressure of urban development has raised the need to identify and foster other urban landscape patches that may contribute to biodiversity conservation. In order to understand the role of urban schools in biodiversity conservation we had two objectives, (i) to determine the exotic and indigenous species richness across schools in low, medium and high density suburbs and (ii) to determine the difference in species diversity across schools in low, medium and high density suburbs. Findings from this study are instrumental in understanding the potential role of schools in biodiversity conservation in urban landscapes.

Means without common superscripts column wise are significantly different.

to the size of the plot of land residents would normally own (Clarke et al., 2013; Endsley et al., 2018; Jenerette et al., 2007). This in turn can be linked to the level of disturbance that would happen to the ecosystem during infrastructural development, where less vegetation is removed in areas with large plots (low density) and almost all the vegetation removed in areas with smaller plots (high density) (Avolio et al., 2015a). Indeed, the area that is left to support planted vegetation is very small in high density suburbs compared with low density suburbs, the “the luxury effect” (Clarke et al., 2013; Jenerette et al., 2007). 2

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Table 2 Estimated regression parameters, standard errors, t-values and P-values for the quasi-Poisson GLM with species richness as the response variable and school yard area as the predictor across the urban gradient (low, medium and high density suburbs) for indigenous trees, exotic trees and garden flowers. Category Indigenous trees

urban gradient Low Medium

Exotic trees

High Low Medium

Garden flowers

High Low Medium High

Parameter

Estimate

Std. error

t-value

p-value

intercept Area intercept Area intercept Area intercept Area intercept Area intercept Area intercept Area intercept Area intercept Area

1.60057 0.00273 1.83735 0.00549 1.83678 −0.00135 2.85256 0.00215 2.30498 0.00327 2.67990 0.00046 3.13657 −0.00081 2.82247 0.00137 2.94042 −0.00482

0.25885 0.00295 0.31155 0.00217 0.32587 0.00670 0.17683 0.00205 0.23738 0.00178 0.15369 0.00312 0.19038 0.00234 0.21172 0.00170 0.35411 0.00743

6.207 0.925 5.898 2.528 5.637 −0.201 16.132 1.052 9.710 1.838 17.437 0.149 16.475 −0.345 13.331 0.805 8.304 −0.648

< 0.001 0.382 < 0.001 0.035 < 0.001 0.846 < 0.001 0.324 < 0.001 0.103 < 0.001 0.885 < 0.001 0.739 < 0.001 0.444 < 0.001 0.535

Fig. 2. Non-metric multidimensional scaling (NMDS), ordination for exotic trees (a), indigenous trees (b), combined indigenous and exotic tree (c) and flowers (d) that were recorded in schools from the low, medium and high density suburbs. R2 is the Non-metric fit.

2. Material and methods

2.2. Field sampling and data collection

2.1. Study area

The city was first stratified into low, medium and high density suburbs based on sizes of residential land area and human population, (≥ 1000 m2 residential land area, with 0-383 people km−2), (500 m2 1000 m2 residential land area, with 386-1207 people km−2) and (< 500 m2 residential land area, with 4331-9222 people km−2), respectively (Chirisa et al., 2015). Ten schools were randomly sampled from each stratum, low, medium and high density suburbs which had 15, 30 and 45 schools, respectively. Data were collected once in October 2017. In order to estimate school yard area, boundaries were digitized in Google earth and the area determined using ArcMap 10.1. Government schools were chosen since all the schools were falling under the same jurisdiction and all were more than 30 years old. The whole school yard was surveyed for garden flowers and trees. Plants

The study was conducted in urban schools of Harare’s low, medium and high density suburbs located between latitude 17˚40' and 18˚00' S, and longitude 30˚55' and 31˚15' E (Fig. 1). Harare falls under agro ecological region II which receives rainfall that ranges between 650–850 mm per annum (Muboko et al., 2014), falling in the summer season, between November and March. The mean temperature ranges from 16 to 22 °C. The area is characterised by sandy loam soils (Harford et al., 2009). The area comprises of vegetation patches that include grasslands, vleis, miombo woodlands dominated by Brachystegia spiciformis Benth. trees and introduced street tree species such as Jacaranda mimosifolia D. Don (Kamusoko et al., 2013). 3

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were classified as either indigenous when occurring naturally in Zimbabwe and exotic when introduced to Zimbabwe from other countries. We did not consider the abundance of garden flowers and trees, but rather we only considered the presence-absence of the species. Both garden flowers and tree species were identified and recorded using the following field guides (Coates, 2005; Graf, 1986, 1976). For those trees and herbs that could not be identified in the field, specimens were taken for identification at the Harare botanical gardens.

Table 3 Results from analysis of similarity (ANOSIM) of indigenous and exotic trees and garden flower assemblages between schools located in the three suburb densities (low, medium and high) in an urban landscape of Harare, Zimbabwe. Factor

R statistic

P-value

Exotic trees global low vs. medium low vs. high medium vs. high Indigenous trees global low vs. medium low vs. high medium vs. high Combined indigenous and exotic trees global low vs. medium low vs. high medium vs. high Flowers global low vs. medium low vs. high medium vs. high

0.045 0.066 0.011 0.042 0.165 0.248 0.053 0.205 0.119 0.169 0.011 0.167 0.125 0.129 0.178 0.08

0.129 0.102 0.409 0.209 0.001 0.002 0.179 0.007 0.004 0.004 0.412 0.004 0.004 0.039 0.005 0.104

2.3. Data analysis One way analysis of variance (ANOVA) was applied to compare school yard size (area) across the urban gradient (low, medium and high density suburbs). A post hoc Tukey HSD was used to determine which two locations were significantly different. The relationship between species richness and area was analysed using quasi-Poisson regression models following procedures in (Zeileis et al., 2008). For multivariate community assemblage analyses, a Bray-Curtis dissimilarity matrix was constructed. Overall differences in tree and garden flowers assemblage composition across the school yards in low, medium Fig. 3. Comparison of sample based interpolation and extrapolation for (a) species richness (q = 0), Shannon diversity (q = 1) and Simpson diversity (q = 2), (b) sample coverage based interpolation and extrapolation for species richness (q = 0), Shannon index (q = 1) and Simpson index (q = 2) and (c) sample based interpolation and extrapolation for sample coverage compared across schools in low, medium and high density suburbs of Harare, Zimbabwe for all trees combined (indigenous and exotic). All extrapolation curves were plotted to three times the sample size, and 300 bootstrap replicates were used to estimate 95% confidence intervals.

4

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Fig. 4. Comparison of sample based interpolation and extrapolation for (a) species richness (q = 0), Shannon diversity (q = 1) and Simpson diversity (q = 2), (b) sample coverage based interpolation and extrapolation for species richness (q = 0), Shannon index (q = 1) and Simpson index (q = 2) and (c) sample based interpolation and extrapolation for sample coverage compared across schools in low, medium and high density suburbs of Harare, Zimbabwe for all the indigenous trees. All extrapolation curves were plotted to three times the sample size, and 300 bootstrap replicates were used to estimate 95% confidence intervals.

and high density suburbs were compared using a one-way analysis of similarity (ANOSIM) with pair-wise comparisons made between suburbs. The R-value obtained from ANOSIM is a measure of dissimilarity and can take a value between -1 and 1, the closer this value is to 1 the more dissimilar the assemblages are. Non-metric multi-dimensional scaling (NMDS) ordinations for trees and flowers were constructed to visually display patterns. Species richness, Shannon diversity and Simpson diversity were computed using the Hill numbers, where q = 0 denotes species richness, q = 1denotes Shannon diversity and q = 2 denotes Simpson diversity. Hill numbers were chosen because of their benefits over other species diversity indices. Some of the benefits of Hill numbers include (i) effective overview to incorporate taxonomic, phylogenetic, and functional diversity, and hence provide a unified framework for measuring biodiversity (Gotelli and Chao, 2013; Chiu and Chao, 2014; Hsieh et al., 2016b) and (ii) in the comparison of multiple assemblages, there is a direct link between Hill numbers and species compositional

similarity or differentiation among assemblages (Jost, 2013). This second property unites diversity and similarity or differentiation. Species richness, Shannon and Simpson indices were computed using formulas outlined by Chiu and Chao (2014). For species richness diversity index is computed as follows: q

S

D= i=1

1

piq

(1 q)

(1)

Where S species is the number of species in the assemblage and species are indexed by i = 1,2,3,…, S. pdenotes the relative frequency of the ith species. The parameter q determines the sensitivity of the measure to the relative frequencies. When q = 0, the abundance of individual species do not contribute to the sum in the equation. The presences are counted so that 0D is simply richness As q approaches 1 (q = 1), equation 1 becomes undefined, however, its limit approaches the exponential of Shannon entropy, that is 5

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Fig. 5. Comparison of sample based interpolation and extrapolation for (a) species richness (q = 0), Shannon diversity (q = 1) and Simpson diversity (q = 2), (b) sample coverage based interpolation and extrapolation for species richness (q = 0), Shannon index (q = 1) and Simpson index (q = 2) and (c) sample based interpolation and extrapolation for sample coverage compared across schools in low, medium and high density suburbs of Harare, Zimbabwe for all the exotic trees. All extrapolation curves were plotted to three times the sample size, and 300 bootstrap replicates were used to estimate 95% confidence intervals.

Shannon index (Chao et al., 2014). The Shannon diversity is therefore given by the equation (Hsieh et al., 2016a,b): 1

D = lim qD=exp ( n

1

(Hsieh and Chao, 2017). 2.3.1. Rarefaction, interpolation and extrapolation We constructed sample-and coverage-based rarefaction and extrapolation curves to determine how the diversity of trees and garden flowers increased with increasing sampling effort and completeness for each stratum. The rarefaction and extrapolation of richness, Shannon diversity, and Simpson diversity were conducted for each method. The Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data for larger and smaller samples (Chao et al., 2014). Sample-based curves evaluated the number of sampling units in a sample by plotting diversity estimates in relation to the number of sampling units. Coverage-based curves were plotted against rarefied sample completeness to illustrate diversity estimates in relation to sample coverage. All extrapolation curves were plotted by

S i=1

pi log pi )

(2)

Simpson diversity index is represented as follows: 2

S

Pi2

D = 1/ i=1

(3)

When q = 2 it yields the inverse of the Simpson concentration referred to as the Simpson diversity (Chiu and Chao, 2014). The measure places more weight on abundant species and strongly discounts rare species. For all q, if qD = u, the diversity of the actual assemblage is the same as that of an idealized assemblage with u equally abundant species (Chiu and Chao, 2014). Species richness, Shannon and Simpson diversity were analyzed using the package iNEXT in the R environment 6

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Fig. 6. Comparison of sample based interpolation and extrapolation for (a) species richness (q = 0), Shannon diversity (q = 1) and Simpson diversity (q = 2), (b) sample coverage based interpolation and extrapolation for species richness (q = 0), Shannon index (q = 1) and Simpson index (q = 2) and (c) sample based interpolation and extrapolation for sample coverage compared across schools in low, medium and high density suburbs of Harare, Zimbabwe for all the flowers. All extrapolation curves were plotted to three times the sample size, and 300 bootstrap replicates were used to estimate 95% confidence intervals.

multiplying the sample size by three. We applied 300 bootstrap replicates to estimate 95% confidence intervals. The 95% confidence intervals were used to determine if differences between strata (low, medium and high density suburbs) were statistically significant. Nonoverlapping 95% confidence intervals, whether rarefied or extrapolated curves are considered to indicate definite significant differences at 5% level (Chao & Jost, 2012; Chao et al., 2014).

competition and historical events (Legendre, 2014). Nestedness is richness difference pattern characterized by the species at a site being a strict subset of the species at a richer site (Legendre, 2014). Beta diversity was computed in the R package betapart (Baselga et al., 2018). 3. Results 3.1. School yard area

2.4. Beta diversity

Area varied significantly across the three suburbs (F2,27 =3.858, p = 0.034). Schools in medium density have significantly larger yards compared with those located in high density suburbs (Table 1). However, there was no difference in school yard area between medium and low density schools as well as between low and high density schools (Table 1).

We compared variation in species composition among schools using the Sorensen index for presence-absence data following the protocol in Baselga and Orme (2012). We computed total dissimilarity (βSOR), as well as the respective turnover (βSIM) and nestedness (βSNE) components (Baselga and Orme, 2012). Species turnover implies the simultaneous gain and loss of species due to environmental filtering, 7

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Fig. 7. Beta diversity (βSOR solid line lines) and its partition into βSIM (dashed lines) and βSNE (dotdash lines) for low (black lines), medium (red lines) and high (blue lines) density suburbs located schools, using 100 samples of 7 sites from each data set for exotic trees (a), indigenous trees (b), combined indigenous and exotic trees (c) and garden flowers (d) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

density with increase in coverage. The increase in species coverage with more school yards sampled was similar for school yards in low and high density suburbs (Fig. 3c). Species coverage became asymptotic after extrapolation (Fig. 3c).

3.2. Relationship between area and species richness A significant positive relationship (t = 2.528, p = 0.035) was only found between school yard area for medium density suburbs and richness of indigenous trees (Table 1). Although not significant, the relationship between school yard area for medium suburbs and species richness for exotic trees and garden flowers was generally positive (Table 2). School yards in the high density suburbs showed very weak relationship with garden flowers, exotic and indigenous trees (Table 2).

3.5. Indigenous tree species diversity Schools in medium density suburbs had highest species richness, Shannon and Simpson diversity indices for indigenous trees compared with those in the low and high density suburbs (Fig. 4a). However, there was no significant difference in tree species richness, Shannon and Simpson indices between low and high density suburbs (Fig. 4a). There was sharp increase in coverage with more schools sampled across sites (Fig. 4c). After sampling 10 schools coverage was similar for schools in the low and medium density suburbs (Fig. 4c). Species coverage became asymptotic after extrapolation (Fig. 4c).

3.3. Assemblage composition A total of 120 tree species belonging to 43 families were identified and recorded across the schools in low, medium and high density suburbs (Appendix A). The exotic tree species composition was highly similar across schools in low, medium and high density suburbs (Fig. 2a, Table 3). Indigenous tree species composition was highly dissimilar across all schools in the three suburbs (Fig. 2b, Table 3), however, trees in low and high density suburbs were similar as reflected by a small R-statistic value and a p-value > 0.05 (Table 3). In addition, species composition for combined (indigenous and exotic) trees was highly similar across sites (Fig. 2c). A total of 89 garden flower species belonging to 41 families were identified and recorded across schools (Appendix B). For all the garden flowers, species composition was highly similar across schools in low, medium and high density suburbs (Fig. 2d).

3.6. Exotic tree species diversity Highest species richness, Shannon and Simpson diversity indices were observed for all the exotic tree species in school yards of low density suburbs compared with those in medium and high density suburbs (Fig. 5a). However there was no significant difference in tree species richness, Shannon and Simpson indices between medium and high density suburbs school yards (Fig. 5a). Species richness and Shannon diversity followed the sequence, low > medium > high density school yards as the sample coverage was increasing (Fig. 5b). We observed a sharp increase in coverage with more school yards sampled in the low density suburbs compared with school yards in the medium and high density suburbs (Fig. 5c). Sample coverage became asymptotic after extrapolation (Fig. 5c).

3.4. Species diversity of all trees (indigenous and exotic) There was no significant difference in tree species richness, Shannon and Simpson indices between school yards of low and high density suburbs for combined indigenous and exotic trees (Fig. 3a). Although there was no difference in tree species richness, Shannon and Simpson indices between school yards in the low and medium density suburbs, school yards from medium density suburbs had significantly higher richness, Shannon and Simpson indices compared with high density (Fig. 3a). Species richness and Shannon diversity followed the order, medium > low > high density suburb school yards as the sample coverage was increasing (Fig. 3b). Simpson diversity did not differ between medium and low density school yards, while both medium and low density school yards had higher Simpson diversity than high

3.7. Garden flower species diversity There was no significant difference in flower species richness, Shannon and Simpson indices between low density and medium density school yards for all the garden flowers (Fig. 6a). Species richness and Shannon diversity followed the order, medium = low > high as the sample coverage was increasing (Fig. 6b). There was sharp increase in coverage with more school yards sampled (Fig. 6c). After sampling 10 school yards coverage was similar for schools in the low and medium 8

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density suburbs (Fig. 6c). Species coverage became asymptotic after extrapolation (Fig. 6c).

a high level of species heterogeneity, which may indicate lack of connectedness between schools as a result of habitat fragmentation, since time of establishment (Socolar et al., 2015). The higher species βSOR and βSIM for indigenous trees compared with exotic trees may reflect the importance of maintaining and growing indigenous trees during urban development since exotic species often quickly homogenize (Socolar et al., 2015). A higher species βSIM for school yards in the high density suburbs compared with low and medium density suburbs reflects high variation in the exotic trees that are being grown at these schools, and follows that a high βSIM occurs when species present at one site are absent at another site, but are replaced by other species absent from the first (Baselga and Orme, 2012; Legendre, 2014; Socolar et al., 2015) The species richness, Shannon and Simpson indices between low density and medium density school yards did not differ for the garden flowers. This could be that they are all exotic being taken from the same pool (urban nurseries where they are bought) (Walker et al., 2009). Furthermore this is also reflected in the lack of any clear relationship between species richness and area of school yards and because all garden flowers were solely planted by schools, their richness is highly independent of school yard area. Very little can be done to avoid this kind of homogenization across the urban landscape. We however, recommend some further qualitative study looking at how the different schools consider which plants to grow in their yards. Considering that African cities have the highest growth rates (UNHabitat, 2012), future contribution of school yards to urban biodiversity conservation is likely compromised. School authorities are most likely to increase the infrastructural development so that they can meet the increasing needs of a growing population at the expense of vegetation. We therefore recommend that schools use the ‘hot sitting model’ where some pupils would attend school from morning to mid-day and others start from mid-day to end of day. This would probably save the areas occupied by trees and garden flowers from infrastructural development. The ‘hot sitting model’ is currently being used in the high density suburbs of Harare and Chitungwiza, Zimbabwe.

3.8. Beta diversity Although beta diversity (βSOR) was similar for the sampled schools across the urban density gradient for exotic trees, school yards of schools from low and medium density suburbs had higher βSNE than school yards of schools from high density suburbs (Fig. 7a). However, school yards from high density suburbs had higher βSIM compared with school yards from low and medium density suburbs (Fig. 7a). For indigenous trees schools yards from medium density suburbs had higher βSOR and βSNE compared with schools yards of schools from low and high density suburbs, while there was no difference in βSIM (Fig. 7b). There was no difference in βSOR and its components, βSNE and βSIM for school yards from low, medium and high density suburbs when we combined indigenous and exotic trees and as well for garden flowers (Fig. 7c,d, respectively). 4. Discussion Our results reveal lack of difference in species composition of garden flowers for schools in the low, medium and high density suburbs. For both indigenous and exotic trees, high density schools had the least richness, Shannon and Simpson diversity indices. For exotic tree species, school yards from low density suburbs had the highest species richness, Shannon and Simpson diversity indices. A plausible explanation is that native trees were cut down and replaced with exotic tree species which are easy to establish, have higher survival rates and grow rapidly (Blood et al., 2016). In addition in affluent areas, people generally plant exotic trees, to provide shade and also to act as wind break (Avolio et al., 2015b), which could have been the reasons why there are more exotic trees in schools in the low density suburbs of Harare. Our findings are consistent with the observation that both socio-economic and environmental drivers are necessary to explain patterns of urban forest composition and cover (Avolio et al., 2015a). As highlighted by Hope et al. (2003) there is a possibility of the “luxury effect” where the richness of exotic trees in school yards from the low density suburbs has been influenced by affluence. Furthermore, preference of exotic species compared with indigenous species in low density suburbs school yards is associated with observed value placed along the walkways to provide shade and to contribute to environmental quality in previous studies (Avolio et al., 2015a,b; Clarke et al., 2013). Species richness, Shannon and Simpson diversity indices for indigenous trees were higher in medium density than in high and low density suburbs school yards. Furthermore, there was higher βSOR and βSNE for medium density school yards. This could be attributed to schools in medium density suburbs having more yard space. Previous studies have shown that richness and diversity are functions of area which is in tandem with current results (Gaston, 2000; Whittaker et al., 2001). Because of the large yard space for schools in the medium density suburbs, large patches of indigenous vegetation were left unaltered during the establishment of schools. In a different study, following a similar gradient focusing on trees in towns, medium density was also shown having higher diversity of indigenous tree species (Johnston, 2012). Schools in medium density suburbs have maintained

5. Conclusion The present study has shown that school yards play a very important role in biodiversity conservation. A total of 120 tree species belonging to 43 families and 89 flower species belonging to 41 families were identified and recorded in school yards from low, medium and high density suburbs. School yards in medium density suburbs had highest species richness, Shannon and Simpson diversity indices for indigenous trees. For all exotics, school yards in low density had the highest species richness, Shannon and Simpson diversity. It is clear from this study that school yards can be centres of biodiversity conservation for both indigenous and exotic plant species in urban landscapes. Homogenization of species richness, Shannon and Simpson indices when exotic trees and flowers are considered shows the nursery trade effect. Author contributions JM, MM designed the study. AK, TM collected the data. JM analysed the data. JM, AK, MM and TM discussed the results and wrote the paper.

Appendix A Table A1

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Table A1 Exotic and indigenous tree species across schools in Harare’s low, medium and high density suburbs. + = present, - = absent. Family

Name of species

Exotic/Indigenous

Low density

Medium density

High density

Adoxaceae Anacardiaceae

Sambucusjavanica Reinw. ex Blume Lannea discolor Engl. Mangifera indica L. Rhus lancea L.f. Rhus tenuinervis Engl. Schinus terebinthifolius Raddi Annona senegalensis Pers. Asimina triloba (L.) Dunal Carissa bispinosa (L.) Merxm. Rauvolfia caffra Sond. Cussonia natalensis Sond. Schefflera actinophylla (Endl.) Harms Arecastrum romanzoffianum Becc. Hyphaene petersiana Klotzsch ex Mart. Araucaria heterophylla (Salisb.) Franco Kigelia africana (Lam.) Benth. Jacaranda mimosifolia D.Don Spathodea campanulata P.Beauv. Tabebuia spp Delonix regia (Bojer) Raf. Casuarina equisetifolia L. Gymnosporia senegalensis Loes. Parinari curatellifolia Planch. ex Benth. Combretum apiculatum Sond. Combretum zeyheri Sond. Terminalia sericea Burch. ex DC. Terminalia stenostachya Engl. & Diels Cypress spp Monotes engleri Gilg Diospyros mespiliformis Hochst. ex A.DC. Euclea crispa (Thunb.) Sond. ex Gürke Manihot esculenta Crantz Croton megalocarpus Hutch. Jatropha curcas L. Ricinus communis L. Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg. Vachellia gerrardii (Benth.) P.J.H.Hurter Vachellia spp Vachellia tortilis (Forssk.) P.J.H.Hurter & Mabb. Vachellia xanthophloea (Benth.) P.J.H.Hurter Bauhiniaur baniana Schinz Bauhinia tomentosa Wall. Brachystegia boehmii Taub. Brachystegia spiciformis Benth. Burkea africana Hook. Cassia abbreviata Oliv. Cassia spp Dichrostachys cinerea (L.) Wight & Arn. Erythrina latissima E.Mey. Erythrina abyssinica Lam. Julbernardia globiflora (Benth.) Troupin Leucaena leucocephala (Lam.) de Wit Peltophorum africanum Sond. Piliostigma thonningii (Schumach.) Milne-Redh. Pterocarpus angolensis DC. Pterocarpus rotundifolius Druce Pterolobium stellatum (Forssk.) Brenan Senna singueana (Delile) Lock Tipuana tipu (Benth.) Kuntze Quercus robur L. Ribes viburnifolium A.Gray Vitex mombassae Vatke Cinnamomum camphora (L.) T.Nees & C.H.Eberm. Persea americana Mill. Strychnos spinosa Lam. Adansonia digitata L. Azanza garckeana (F.Hoffm.) Exell & Hillc. Grewia occidentalis L. Trichilia emetica Vahl Khaya nyasica Stapf ex Baker f. Melia azedarach L. Trichilia dregeana Harv. & Sond. Artocarpus altilis (Parkinson) Fosberg Ficus benjamina L.

Exotic Indigenous Exotic Indigenous Indigenous Exotic Indigenous Exotic Indigenous indigenous Indigenous Exotic Exotic Indigenous Exotic Indigenous Exotic Exotic Exotic Exotic Exotic Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Exotic Indigenous Indigenous Indigenous Exotic Exotic Exotic Exotic Exotic Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Exotic Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Exotic Exotic Exotic Indigenous Exotic Exotic Indigenous Indigenous Indigenous Indigenous Indigenous Indigenous Exotic Indigenous Exotic Exotic

+ – + – – + – + + + – + + + + + + + – + – – + + – – – + – + – + + + + + + – + + + + – + – – + – – + + + – – – – – – + + – – + + + + + + + + + + + +

– + + – + + + + – – + – – + + + + + – + + + + + + + + – + – + + + + + – + – – – + + + + + + + + + + + + + + + + + + + – + + + + + – + + + + + + – +

– – + + + + – – + – – – – + + + + + + + – + + – – – – – – – – + + + + – – + – + + + – + – – + + – + + + – – + + – – + – – – + + + – + – + + + + – +

Annonaceae Apocynaceae Araliacea Arecaceae Arucariaceae Bignoniaceae

Caesalpiniaceae Casuarianaceae Celastraceae Chrysobalanaceae Combretaceae

Cupressaceae Dipterocarpaceae Ebenaceae Euphorbiaceae

Fabaceae

Fagaceae Grossulariaceae Lamiaceae Lauraceae Loganiaceae Malvaceae Meliaceae

Moraceae

(continued on next page) 10

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Table A1 (continued) Family

Moringaceae Myrtaceae

Musaceae Phyllanthaceae Pinaceae Proteacea Punicaceae Rhamnaceae

Rosaceae

Rubiaceae Rutaceae

Salicaceae Solanaceae Teliaceae Verbenaceae Vitaceae Zingiberaceae

Name of species

Exotic/Indigenous

Low density

Medium density

High density

Ficus elastica Roxb. Ficus natalensis Hochst. Ficus spp Ficus stuhlmannii Warb. Ficus sur Forssk. Ficus sycomorus L. Morus nigra L. Moringa oleifera Lam. Callistemon acuminatus Cheel Eucalyptus grandis W.Hill Eucalyptus spp Callistemon viminalis (Gaertn.) G.Don Psidium guajava L. Syzygium cordatum Hochst. Syzygium guineense DC. Musa acuminata Colla Pseudolachnostylis maprouneifolia Pax Uapaca kirkiana Müll.Arg. Pinus patula Schltdl. & Cham. Grevillea robusta A.Cunn. Macadamia tetraphylla L.A.S.Johnson Punica granatum L. Ziziphus abyssinica Hochst. ex A.Rich. Ziziphus mauritiana Lam. Ziziphus mucronata Willd. Berchemia discolor Hemsl. Eriobotrya japonica (Thunb.) Lindl. Malus pumila Poit. & Turpin Prunus cerasus Scop. Prunus domestica L. Prunus persica (L.) Batsch Coffea spp Gardenia spp Vangueria infausta Burch. Casimiro aedulis La Llave Citrus limon (L.) Osbeck Citrus reticulata Blanco Citrus sinensis Pers. Flacourtia indica (Burm.f.) Merr. Brugmansia suaveolens (Willd.) Sweet Grewia monticola Sond. Citharexylum spinosum L. Lantana camara L. Lippia javanica Spreng. Vitis vinifera L. Aframomum angustifolium K.Schum.

Exotic Indigenous Indigenous Indigenous Indigenous Indigenous Exotic Exotic Exotic Exotic Exotic Exotic Exotic Indigenous Indigenous Exotic Indigenous Indigenous Exotic Exotic Exotic Exotic Indigenous Exotic Indigenous Indigenous Exotic Exotic Exotic Exotic Exotic Exotic Exotic Indigenous Exotic Exotic Exotic Exotic Indigenous Exotic Indigenous Exotic Exotic Indigenous Exotic Indigenous

– – – – – + + + – + + + + – + + – – + + + + + – – – + + + + + + + – + + + + – – – + + + – –

+ + + + + + + – + + – + + + + + + + + + – – + + + + + – + – + – – + + + + + + + + – + – – +

– – – + – + + + – + + – + + + + – + + + – + + – – + + + + + + – – – + + + + – – – + + – + –

Appendix B Table B1

11

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Table B1 Flowers species present across schools in Harare’s low, medium and high density suburbs. + = present, - = absent. Family

Name of Species

Exotic\ Indigenous

Low Density

Medium Density

High Density

Acanthaceae Agavaceae Aizoaceae

Sanchezia speciosa Leonard Agave attenuata Salm-Dyck Aptenia cordifolia (L.f.) Schwantes Carpobrotus edulis N.E.Br. Aloe dawei A.Berger Iresine herbstii Hook. Alternanthera spp Agapanthus spp Hippeastrum spp Catharanthus roseus (L.) G.Don Nerium oleander L. Plumeria rubra L. Thevetia peruviana K.Schum. Colocasia esculenta (L.) Schott Alocasia odora (Roxb. ex Lodd., G.Lodd. & W.Lodd.) Spach Alocassia spp Monstera deliciosa Liebm. Sygonium spp Syngonium podophyllum Schott Zantedeschia aethiopica Spreng. Schefflera arboricola Hayata Asparagus densiflorus (Kunth) Jessop Asparagus spp Chlorophytum comosum Baker Cordyline fruticosa Göpp. Dracaena deremensis Dracaena Dracaena spp Sansevieria trifasciata hort. ex Prain Bulbine frutescens Willd. Artemisia absinthium L. Artemisia afra Jacq. ex Willd. Bellis perennis L. Chrysanthemum spp Gazania spp Gerbera jamesonii Adlam Osteospermum spp Tagetes erecta L. Wedelia trilobata (L.) Hitchc. Zinnia spp Begonia spp Tecoma stans Juss. Alysum spp Canna indica L. Saponaria spp Dianthus spp Tradescantia pallida (Rose) D.R.Hunt Tradescantia spp Ipomoea spp Echeveria elegans (Rose) A.Berger Kalanchoe beharensis Drake Kalanchoe sexangularis N.E.Br. Kalanchoe spp Kalanchoe thyrsiflora Harv. Sedum nussbaumerianum Bitter Sedum spp Cyperus digitatus Roxb. Polystichum munitum (Kaulf.) C.Presl Acalypha wilkesiana Mull.Arg. Acalypha marginata Spreng. Euphorbia cotinifolia L. Euphorbia hyperia Euphorbia milii Des Moul. Euphorbia spp Euphorbia pulcherrima Willd. ex Klotzsch Geranium renardii Trautv. Hydrangea macrophylla (Thunb.) Ser. Glandilus spp Jasmine jasmine L. Coleus blumei Benth. Lavendula spp Salvia splendens Sellow ex Schult. Vitex trifolia variegata Liriope muscari L.H.Bailey Cuphea hyssopifolia Kunth

Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic

– + + + – + + + – + + + + – + – + + + – – – + – + – + + + + – + – + – + + + + + + + + + + + – – – + – + + + + – + – + + + – – + + – + – + + + + + +

+ + – + – + + + – + + + + + – + + + + – + + + + + + + + + + – + + – – + + – – – + + + – + + + + + + + + + – – + + + + + – – – + + + + + + – + + + –

– + – + + + – + + + + + – – – – + – + + + – + + + – + + + + + + – + + + + – + + + – + – + + – – – + + + + – – – + + + + + + + + + – + – + – – + – –

Aloaceae Amaranthaceae Amaryllidaceae Apocynaceae

Araceae

Araliaceae Asparagaceae

Asphodelaceae Asteraceae

Begoniaceae Bignoniaceae Brassicaceae Cannaceae Caryophyllaceae Comelinaceae Convolvulaceae Crassulaceae

Cyperaceae Dyopteridaceae Euphorbiaceae

Geraniaceae Hydrangeaceae Iridaceae Jasmineae Lamiaceae

Lillaceae Lythraceae

(continued on next page) 12

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Table B1 (continued) Family

Name of Species

Exotic\ Indigenous

Low Density

Medium Density

High Density

Malvaceae Nyctaginaceae Oleaceae Polyganaceae Rosaceae Scrophulariaceae Solanaceae

Hibscus rosa sinensis L. Bougainvillea glabra Choisy Ligustrum ovalifolium Hort ex Decne. Polygonum spp Rosa damascena Mill. Antirrhinum majus L. Brunfelsia pauciflora Benth. Petunia glandiflora Petunia spp Solanum nigrum L. Streptosolen jamesonii Miers Strelitzia nicolai Regel & Körn. Tropaeolum majus L. Duranta erecta L. Viola tricolor L.

Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic Exotic

+ + + + + – + + + + – + + + –

+ + + – + + + + + – + + + + +

+ + + – + – + – + – – – + + +

Strelitziaceae Trapaeolaceae Verbenaceae Violaceae

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