46 Plant and Kendal: Resident Tolerance for Mixtures of Tree Species Within Streets which explained 65.1% of pricing variance across the sample. While species diversity (measured using the Shannon Index) had no significant effect on house price, species richness (number of species) was sig- nificant and a negative predictor of house price. The greater the number of different tree species in the street, the lower the house sale price. Each additional street tree within 100 m of a house sale site added $682 to median house sale price, but when that addi- tional tree was a new species, house sale price was $2,625 lower. In the third-stage models, a threshold of no more than six species reversed this negative effect to a significant positive effect. Six or fewer different species nearby added $15,015 (or 2.9%) to median house sale price. The presence of some mature or aged street trees nearby added greatest value (i.e., $17,168 or 3.3% above median house sale price). Results of the first three stages of revealed preference analysis are shown in Table 2. In summary, home-buyers were expressing their preference for more street trees, especially of mature age, but there was a threshold of tolerance for species mix within streetscapes nearby, where more than six species had a negative effect on house prices. Of all the location characteristics in the data set, only household income and education levels of suburbs had significant, although weak, correlations (using Pearson’s 2-tailed t-test) with street tree species richness nearby (suburb income × richness = 0.060 at p < 0.01; suburb education × richness = 0.067 at p < 0.01). When tested within the models, these location variables sig- nificantly influenced the value that home-buyers placed on a limited mix of species within streets. In locations with a greater proportion of households with education levels at year 12 or above, home-buyers were willing to pay an additional premium of 9.1% above other suburbs for houses with streetscapes with six street tree species or less nearby. In locations with a greater proportion of households with income lev- els in the top quartile, the premium was 3.1% above other suburbs (Table 3). DISCUSSION This study explored one of the many types of human- environment interactions (Williams 2002; Ives and Kendal 2014; Avolio et al. 2015; Kabisch et al. 2015; Säumel et al. 2016), that must also be taken into account when seeking to diversify and sustain street tree populations (i.e., resident preferences). We found that home-buyers expressed a preference for more street trees nearby, especially if those trees were mature or aged. However, additional tree species nearby, beyond six species, discounted house sale prices. This threshold of tolerance for species diver- sity varied by location, with less diversity preferred in locations of greater socio-economic advantage. Low levels of street tree species diversity found across many cities, despite a large species pool to choose from, suggest that there are strong drivers which limit diversity in roadside environments. Biophysical chal- lenges presented by the harsh, varied, and constrained nature of roadsides can limit the number of species that can tolerate such conditions in any climate (Jim 1998; Kendal et al. 2014; Miller et al. 2015). Our study suggests that resident preferences are well adapted to these low levels of species richness, and strategies such as trialling native species (Sjöman et al. 2016), expanding municipal species lists (Laćan and McBride 2008), and encouraging nurseries to Table 3. Summary of interactions between tolerance for species mixtures within streets and socio-economic levels of suburbs. Variables of interest R2 Suburb household income Suburb household education D_Species richness ≤ 6 D_Species richness ≤ 6 X income D_Species richness ≤ 6 X education * p < 0.1; ** p < 0.05; *** p < 0.01 ©2019 International Society of Arboriculture Base model 0.6513 Coeff. 0.0204 *** 0.0086 *** 0.0286 ** Income R2 Coeff. 0.0226 *** 0.0084 *** 0.0629 *** -0.0031 * 0.6513 Interactive models R2 % change Education 0.6513 Coeff. 0.0208 *** 0.0095 *** 0.1213 ** -0.0018 * < 1% < 1% 3.12% 9.09%
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