Description:
<jats:title>ABSTRACT</jats:title><jats:p>Changes in phenology are indicators of climate change. Urban land use influences local climates through mechanisms such as urban heat island (<jats:styled-content style="fixed-case">UHI</jats:styled-content>) effects. This research examined the spatio‐temporal variations in first flowering with changes in urban land use in England and Wales. It used standard ordinary least squares (<jats:styled-content style="fixed-case">OLSs</jats:styled-content>) regressions and geographically weighted regressions (<jats:styled-content style="fixed-case">GWRs</jats:styled-content>) to analyse changes in phenophase observation date between 1934 and 2007 for three tree species. The <jats:styled-content style="fixed-case">OLS</jats:styled-content> models suggested that first flowering was getting earlier: blackthorn by 0.28 days per year, hawthorn by 0.16 days per year and horse chestnut by 0.13 days per year. These rates were found to vary spatially when <jats:styled-content style="fixed-case">GWR</jats:styled-content> was used and the greatest rates of change were found to be highly localized. The addition of land use change was found to improve the model fit and suggested that a 10% increase in urban land use was also associated with phenophase advancement of 1.20 days for blackthorn, 0.57 days for hawthorn and 0.90 days for horse chestnut. When the impacts of urban land use changes was analysed using <jats:styled-content style="fixed-case">GWR</jats:styled-content>, the associations with phenophase advancement were found to vary spatially, strongest associations were generally more pronounced in the north and especially in the extreme south‐west and the north for all species. The results of this research suggest that the impacts of climate changes and the effects of urban land use changes on phenology vary spatially and that the impacts of urban expansion, such as <jats:styled-content style="fixed-case">UHI</jats:styled-content> effects, may not be uniform. These findings suggest the need for spatially explicit analyses to quantify the local impacts and drivers of climate changes and their associated feedbacks.</jats:p>