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Do forest edges reduce timber productivity - Implications for retention forestry techniques

Baker, TP, Scott, RE, Neyland, MG ORCID: 0000-0002-9755-9438 and Musk, RA 2019 , 'Do forest edges reduce timber productivity - Implications for retention forestry techniques' , Forest Ecology and Management, vol. 448 , pp. 208-217 , doi:

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Retention forestry techniques such as aggregated retention rely on proximity to an adjacent standing forest to increase the speed and success of biological community regeneration post-disturbance. However, increased competition near a standing forest may result in a decrease in the growth of targeted timber species which in turn may impact on timber yields. This study examined the impact that standing forest adjacent to regenerating forest has on the height of Eucalyptus spp. regeneration in the wet temperate forests of Tasmania, Australia. We used LiDAR data to test whether the height of the regenerating forest varied with proximity to the adjacent forest following harvesting and regeneration treatments. The distance response was examined across two silvicultural systems, aggregated retention (ARN) and clearfell, burn and sow (CBS), and across two broad age classes (1–7 years and 11–24 years). Height growth was reduced within 23 m of a retained edge, with a maximum reduction of 12% occurring closest to the edge. The edge effect was similar across ages and between silvicultural systems, although ARN silviculture will suffer greater losses compared to CBS due to the greater percentage of area adjacent to a standing forest. Understanding productivity losses associated with increasing edges in harvesting systems, provides important information for forest managers balancing economic and biodiversity conservation requirements.

Item Type: Article
Authors/Creators:Baker, TP and Scott, RE and Neyland, MG and Musk, RA
Keywords: LiDAR, regeneration forest, tree height, edge proximity, edge effect
Journal or Publication Title: Forest Ecology and Management
Publisher: Elsevier Science Bv
ISSN: 0378-1127
DOI / ID Number:
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© 2019 Elsevier B.V. All rights reserved.

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