Quantifying floristic and structural forest maturity: An attribute‐based method for wet eucalypt forests. Issue 4 (23rd March 2018)

• Record Type: Journal Article
• Authors: van Galen, Laura G.; Jordan, Gregory J.; Musk, Robert A.; Beeton, Nicholas J.; Wardlaw, Timothy J.; Baker, Susan C.
• Editors: Fajardo, Alex
• Is Part Of: Journal of applied ecology. Volume 55:Issue 4(2018)
• Page Start: 1668
• Page End: 1681
• Publication Date: 2018-03-23
• Subjects: community dynamics -- ecological succession -- floristic composition -- forest management -- forest maturity -- LiDAR -- old‐growth -- structural complexity; Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.13133
• Languages: English
• ISSNs: 0021-8901

Abstract

Abstract: Maintaining developmental heterogeneity of ecological communities within landscapes is crucial for sustainable native forest management. Consequently, methods to assess forest maturity (i.e. the degree to which the forest contains attributes and supports processes characteristic of late‐successional forests) are valuable for making management decisions. However, no consistent, pragmatic method to quantify maturity that incorporates multiple ecosystem elements is available for many forest systems, including Australian wet eucalypt forests. We draw upon forest community dynamics theory to develop a method to quantify maturity based on forest attributes, and use this method to create two metrics of wet eucalypt forest floristic and structural maturity. We then test the ability of remotely sensed and field‐collected variables to predict these metrics. Both the floristic and structural maturity metrics performed well at capturing underlying trends of forest maturation. Remotely sensed LiDAR (Light Detection and Ranging) and photo‐interpretation data provided estimates of moderate accuracy for both floristic and structural maturity ( R 2 = .57–.77). Field variables that are relatively efficient and accurate to measure provided greater model accuracy ( R 2 = .73–.85). Including more complex field variables increased model accuracy to high levels ( R 2 = .93). Therefore, while maturity predicted from remote‐sensing data enables a useful and accessible large‐scale maturity measure, field indices would provide a more accurate means of assessing maturity at the local stand level. Synthesis and applications . The metrics developed in this study provide a powerful tool for undertaking consistent assessments of wet eucalypt forest maturity. This assessment tool could improve forest management by providing information to optimise practices such as prioritising stands for retention or harvesting, determining the effectiveness of restoration or management practices and monitoring changes in maturity over time. The method could be adapted to any forest system that undergoes well‐defined directional development. Abstract : The metrics developed in this study provide a powerful tool for undertaking consistent assessments of wet eucalypt forest maturity. This assessment tool could improve forest management by providing information to optimise practices such as prioritising stands for retention or harvesting, determining the effectiveness of restoration or management practices and monitoring changes in maturity over time. The method could be adapted to any forest system that undergoes well‐defined directional development.