2019
DOI: 10.1002/eap.1968
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Estimating historical forest density from land‐survey data: a response to Baker and Williams (2018)

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Cited by 10 publications
(20 citation statements)
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References 30 publications
(70 reference statements)
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“…Levine et al. (; LE hereafter) claim our landscape‐scale method for reconstructing historical forests fails, based on updated modern tests in six disjunct small plots, half located in highly altered forests, but here we show our method works even at these inadequate sites. LE is a correction of Levine et al.…”
Section: Introductionmentioning
confidence: 52%
“…Levine et al. (; LE hereafter) claim our landscape‐scale method for reconstructing historical forests fails, based on updated modern tests in six disjunct small plots, half located in highly altered forests, but here we show our method works even at these inadequate sites. LE is a correction of Levine et al.…”
Section: Introductionmentioning
confidence: 52%
“…However, due to the lack of a correction factor that accounts for the number of trees used to estimate density at individual sampling points, methods developed by Williams and Baker (2011) overestimated tree densities by 24-667% for contemporary stands with known densities (Levine et al 2017(Levine et al , 2019. Levine et al (2017Levine et al ( , 2019 enabled independent evaluation of their methods and data by archiving all GLO estimator code and data on publicly accessible websites; data and code supporting Williams and Baker (2011) are not similarly accessible (Stephens et al 2021). Independently validated methods for estimating tree density from point data were shown to yield estimates that were less biased (Levine et al 2017) as well as more consistent with tree-ring reconstructions and less than half as large (Johnston et al 2018) as those produced using Williams and Baker (2011) methods.…”
Section: Misrepresented Historical Forest Conditionsmentioning
confidence: 99%
“…[2018], Levine et al. [2019]). That said, these large‐scale historical timber surveys are a robust source of quantitative data that require very few assumptions to generate forest structure and composition metrics (Hagmann et al.…”
Section: Discussionmentioning
confidence: 99%
“…These areas with low live overstory cover can be quite important for maintaining high biodiversity (White et al 2015). These limitations further emphasize that all historical forest reconstructions, including this one, are incomplete (e.g., Collins et al [2018], Levine et al [2019]). That said, these large-scale historical timber surveys are a robust source of quantitative data that require very few assumptions to generate forest structure and composition metrics (Hagmann et al 2018).…”
Section: Discussionmentioning
confidence: 99%