Quantifying forest structural diversity based on large-scale inventory data: a new approach to support biodiversity monitoring

摘要

Background:The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised.However,tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed,for example to support biodiversity monitoring.The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types,typically using only small data sets.Results:Here we developed an index of structural diversity based on National Forest Inventory (NFI) data of Baden Württemberg,Germany,a state with 1.3 million ha of diverse forest types in different ownerships.Based on a literature review,11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity.An initial comprehensive list of 52 variables derived from National Forest Inventory (NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity.These variables comprise 1) quadratic mean diameter at breast height (DBH),2) standard deviation of DBH,3) standard deviation of stand height,4) number of decay classes,5) bark-diversity index,6) trees with DBH ≥ 40 cm,7) diversity of flowering and fructification,8) average mean diameter of downed deadwood,9) mean DBH of standing deadwood,10) tree species richness and 11) tree species richness in the regeneration layer.These variables were combined into a simple,additive index to quantify the level of structural diversity,which assumes values between 0 and 1.We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions:The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.