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A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain.

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Title: A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain.
Authors: Ray, Duncan1 duncan.ray@forestresearch.gov.uk, Marchi, Maurizio2, Rattey, Andrew1, Broome, Alice1
Source: Ecology & Evolution (20457758). Jul2021, Vol. 11 Issue 14, p9423-9434. 12p.
Subject Terms: *Forest site quality, *Forests & forestry, *Forest management, *Forest surveys, *Forest policy, *Land cover
Abstract: Interactions between soil, topography, and climatic site factors can exacerbate and/ or alleviate the vulnerability of oak woodland to climate change. Reducing climaterelated impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) "published regional reports" of oak woodland area; and an "NFI map" of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from "NFI survey" sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice. [ABSTRACT FROM AUTHOR]
Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain.
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  Data: Interactions between soil, topography, and climatic site factors can exacerbate and/ or alleviate the vulnerability of oak woodland to climate change. Reducing climaterelated impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) &quot;published regional reports&quot; of oak woodland area; and an &quot;NFI map&quot; of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from &quot;NFI survey&quot; sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p &lt; .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Ecology &amp; Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1002/ece3.7752
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 9423
    Subjects:
      – SubjectFull: Forest site quality
        Type: general
      – SubjectFull: Forests & forestry
        Type: general
      – SubjectFull: Forest management
        Type: general
      – SubjectFull: Forest surveys
        Type: general
      – SubjectFull: Forest policy
        Type: general
      – SubjectFull: Land cover
        Type: general
    Titles:
      – TitleFull: A multi-data ensemble approach for predicting woodland type distribution: Oak woodland in Britain.
        Type: main
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            NameFull: Ray, Duncan
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            NameFull: Marchi, Maurizio
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            NameFull: Rattey, Andrew
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            NameFull: Broome, Alice
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            – D: 15
              M: 07
              Text: Jul2021
              Type: published
              Y: 2021
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              Value: 11
            – Type: issue
              Value: 14
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            – TitleFull: Ecology & Evolution (20457758)
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