A multi‐data ensemble approach for predicting woodland type distribution: Oak woodland in Britain. Issue 14 (21st June 2021)

• Record Type: Journal Article
• Authors: Ray, Duncan; Marchi, Maurizio; Rattey, Andrew; Broome, Alice
• Is Part Of: Ecology and evolution. Volume 11:Issue 14(2021)
• Page Start: 9423
• Page End: 9434
• Publication Date: 2021-06-21
• Subjects: biomod2 -- national forest inventory -- Quercus robur -- Quercus petraea -- species distribution model; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.7752
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate‐related 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 : The manuscript provides a method to predict a thematic woodland type within broad habitat woodland classes, using oak dominated woodland as a case study. The approach uses different woodland spatial and non‐spatial data with the biomod2 ensemble model. Good results were obtained by the approach, and the resulting oak woodland distribution map will be used to evaluate the interactions between climate, topography and soil conditions under climate change. This will help develop timely adaptation management plans to reduce impacts.