2020
DOI: 10.1016/j.ufug.2019.126580
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Using big data to improve ecotype matching for Magnolias in urban forestry

Abstract: This is a repository copy of Using big data to improve ecotype matching for Magnolias in urban forestry.

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Cited by 15 publications
(13 citation statements)
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“…This is likely to bias the evaluation of species in specialist horticultural texts (heuristic literature), particularly for taxonomic groups that are not well studied. As demonstrated in the genus Magnolia , the use of species distribution models (SDMs) that integrate observational and climate data offer opportunities to identify new species’ ecotypes (provenances) from which to recruit plant material for urban forestry (Watkins et al., 2020). These models in combination with plant functional trait data offer exciting opportunities to evaluate woody flora for future climate scenarios.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This is likely to bias the evaluation of species in specialist horticultural texts (heuristic literature), particularly for taxonomic groups that are not well studied. As demonstrated in the genus Magnolia , the use of species distribution models (SDMs) that integrate observational and climate data offer opportunities to identify new species’ ecotypes (provenances) from which to recruit plant material for urban forestry (Watkins et al., 2020). These models in combination with plant functional trait data offer exciting opportunities to evaluate woody flora for future climate scenarios.…”
Section: Discussionmentioning
confidence: 99%
“…For other species, discrepancies between the apparent drought tolerance indicated by summer leaf Ψ P0 and the tolerance index based on crown condition may be that those species that avoid drought through isohydric behavior (Martínez‐Vilalta & Garcia‐Forner, 2017) or deep rooting strategies (Canadell et al., 1996) do not necessarily have an ability to cope with low leaf water potential. Variation in putative drought tolerance may also arise as a function of data being derived from different ecotypes or provenances (Watkins et al., 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Among others, their contribution to air pollution removal (Grote et al 2016;Nowak et al 2006;Pace et al 2020), cooling by transpiration and shading (Rahman et al 2020a, b), habitat and biodiversity maintenance (Alvey 2006;Angold et al 2006;Muller et al 2010) and human health (Kan and Chen 2004;Tzoulas et al 2007) is of particular interest. The impact of global climate warming (McCarthy et al 2010) may be exacerbated by the urban heat island effect (An et al 2020;Moser et al 2018a, b) which both suggest farsighted future tree species selection for urban areas (Ferrini et al 2014;Khan et al 2020;Roloff et al 2009;Watkins et al 2020). In this context, Rötzer et al (2019) integrated the knowledge about the tree growth and size-dependent ecosystem services provision into a process-based model at the tree level.…”
Section: Introductionmentioning
confidence: 99%
“…Alongside the barriers presented by working with an emerging theoretical field, the silo thinking that exists within urban forestry research needs to be overcome. Practitioners still place considerable emphasis on the heuristic literature (i.e., based on experience rather than standardised trials per Watkins et al, 2020), resulting in a slow feedback loop between research and practise, with tree nurseries yet to report changing sales patterns in spite of recent advances in the scope and focus of species selection guidance (e.g., Hirons and Sjöman, 2018). Where attempts have been made to use Plant Functional Types (PFT) in urban forestry, these have predominantly used traits that describe hypothetical ecosystem service delivery (Baraldi et al, 2019;Núñez-Florez et al, 2019) or include aesthetic criteria (Conway and Vander Vecht, 2015;Goodness et al, 2016;Goodness, 2018) rather than the fundamental ability of a tree to persist in an environment, which is the prerequisite for any other values that a tree might provide once it is mature.…”
Section: Introductionmentioning
confidence: 99%