Tree growth x elevation and latitude relationships from the literature

We used two approaches to find papers. First, using Google Scholar and ISI Web of Science, we searched the literature for studies of tree growth, especially via diameter or ring width, by elevation or latitude. Secondly, we queried colleagues in the field of dendrochronology and related fields for suggestions of papers that would have such data. Of the 20 papers (Babst et al., 2013; Bhuta et al., 2009; Cavin & Jump, 2017; Cook & Cole, 1991; Cook et al., 1998; Coomes & Allen, 2007; de Sauvage et al., 2022; Gantois, 2022; Gillman et al., 2015; Hikosaka et al., 2021; Huang et al., 2010; King et al., 2013; Klesse et al., 2020; Liang et al., 2019; Martin- Benito & Pederson, 2015; Oleksyn et al., 1998; Rapp et al., 2012; Wang et al., 2017; Zhou et al., 2022; Zhu et al., 2018) we found for these relationships, six included clear raw tree data in either scatterplots or tables that we scraped: Oleksyn et al. (1998); Huang et al. (2010); Cavin & Jump (2017); Wang et al. (2017); Zhu et al. (2018); Zhou et al. (2022). We could not scrape data from 14 papers for the following reasons: 1. Absence of observational tree growth raw data: Some studies only presented the correlation or the data was modeled. 2. Measures other variables: Some studies examined leaf area index and forest NPP. 3. Standardization of tree growth with other variables: Papers did not present the raw data (e.g., papers presented the data calculated with other variables). 4. Presence of overlapping data points: Data points in the plots presented were not visually identifiable for accurate data scraping. 5. Line graphs: No discrete data points for image processing. 6. Geographical scale: The locations of data collection spread across large longitudinal or latitudinal gradient. We scraped tree growth data from the selected studies using the Fiji image processing package with the Figure Calibration plugin. 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