©2023 International Society of Arboriculture 192 Tabassum et al: A Plant Selection Tool for Changing Urban Climates settings, such as i-Tree (Nowak et al. 2018), Citree (Vogt et al. 2017), and Tree Species Selection for Green Infrastructure: A Guide for Specifiers (Hirons and Sjöman 2019). These tools contain in-depth information regarding species’ appearance, environ- mental tolerances, planting requirements, risks, and benefits. However, plant selection tools based on the suitability of species for future climates are generally lacking, with the few that exist using only 1 or 2 cli- mate variables to match species’ climate tolerances with future projected climates (Yang 2009; Kendal et al. 2017; Brandt et al. 2021). Here, we describe the creation of Which Plant Where, a comprehensive plant selection tool for supporting resilient, climate- ready, urban green spaces in Australia. The tool allows practitioners to: (1) select species for urban greening projects that have been projected by species distribu- tion models to tolerate climates for the periods 2030, 2050, and 2070; (2) diversify their planting palettes by browsing similar climatically suitable species; and (3) assess the benefits afforded by selecting palettes of plant species. MATERIALS AND METHODS Species Selection To create Which Plant Where, we compiled a com- prehensive list of species, encompassing both com- mon species in the horticultural trade as well as less common or underused species. Species were selected from multiple sources, including a list of species most commonly grown in Australian nurseries (M. Plum- mer, personal communication) and feedback from extensive stakeholder engagement regarding species types and underutilised species. Following stake- holder feedback, we placed a greater emphasis on native (two-thirds of the species list) and woody (two-thirds of the species list) species. We included species, subspecies, cultivars, varieties, genus culti- vars, hybrids, and hybrid cultivars from various plant growth forms including trees, shrubs, climbers, her- baceous species, and graminoids sold and grown throughout Australia and planted in urban landscapes. To standardise the taxonomy, the species list was first checked against the backbone taxonomy of the Global Biodiversity Information Facility (GBIF) and then against The Plant List (TPL) using the Taxonstand package in R v.3.6.2 (R Core Team 2019). Modelling For Which Plant Where, we utilised 2 approaches to estimate the climate suitability of species at the post- code level (n = 2648). Postcodes in Australia largely correspond to suburbs, and through stakeholder engagement events, it was agreed that the postcode was the smallest geographic space over which most of the users of the tool would be operating. For most species (n = 1377), we fitted species distribution models (SDMs). For species for which SDMs either could not be fitted, due to lack of occurrence data, or had poor accuracy, we used a simplified approach (the niche method)(n = 463). Models were fitted at the species level only, due to the lack of robust occur- rence data for cultivars, hybrids, and subspecies. All analyses were undertaken in R v.3.4.4 (R Core Team 2019). A full description of modelling methods can be found in Burley et al. (2019) and are briefly described below. SDM Method: Occurrence Records For each species, global occurrence records were downloaded from GBIF and the Atlas of Living Aus- tralia (ALA). These records were cleaned to remove duplicates and errors. SDM Method: Climate Data We obtained climate data from CHELSA v.1.2 (Karger et al. 2017). CHELSA contains 19 climatic variables (Appendix) summarised for the baseline (climate data averaged from 1979 to 2015) and 2 future time periods (2050 and 2070). These data were at a spatial resolution of approximately 1 km. We selected 6 of the 19 variables with correlations below 0.7 to fit models: annual mean temperature, tempera- ture seasonality, maximum temperature of the warm- est month, annual precipitation, precipitation of the driest month, and precipitation seasonality. However, due to poor model performance for 312 species (found mainly in Southwest and Western Australia), we employed an alternative set of bioclimatic predic- tors to maximise model performance (annual mean temperature, maximum temperature of the warmest month, mean temperature of the coldest quarter, annual precipitation, and precipitation of the driest month). To assess the impacts of climate change, data from multiple global climate models with low inter- dependence should be used to improve accuracy of model predictions (Beaumont et al. 2008; Taylor et al. 2012; Baumgartner et al. 2018). Hence, we used 10
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