Aim
Forest carbon storage is the result of a multitude of interactions among biotic and abiotic factors. Our aim was to use an integrative approach to elucidate mechanistic relationships of carbon storage with biotic and abiotic factors in the natural forests of temperate Australia, a region that has been overlooked in global analyses of carbon‐biodiversity relations.
Location
South‐eastern Australia.
Time period
2010–2015.
Major taxa studied
Forest trees in 732 plots.
Methods
We used the most comprehensive forest inventory database available for south‐eastern Australia and structural equation models to assess carbon‐storage relationships with biotic factors (species or functional diversity, community‐weighted mean (CWM) trait values, structural diversity) and abiotic factors (climate, soil, fire history). To assess the consistency of relationships at different environmental scales, our analyses involved three levels of data aggregation: six forest types, two forest groups (representing different growth environments), and all forests combined.
Results
Structural diversity was consistently the strongest independent predictor of carbon storage at all levels of data aggregation, whereas relationships with species‐ and functional‐diversity indices were comparatively weak. CWMs of maximum height and wood density were also significant independent predictors of carbon storage in most cases. In comparison, climate, soil, and fire history had only minor and mainly indirect effects via biotic factors on carbon storage.
Main conclusions
Our results indicate that carbon storage in our temperate forests was underpinned by tree structural diversity (representing efficient utilisation of space) and by CWM trait values (representing selection effects) more so than by tree species richness or functional diversity. Abiotic effects were comparatively weak and mostly indirect via biotic factors irrespective of the environmental range. Our study highlights the importance of managing forests for functionally important species and to maintain and enhance their structural complexity in order to support carbon storage.
Introduction: This study depicts broad-scale revegetation patterns following sand mining on North Stradbroke Island, south-eastern Queensland, Australia. Methods: Based on an ecological timeline spanning 4-20 years post-rehabilitation, the structure of these ecosystems (n = 146) was assessed by distinguishing between periods of 'older ' (pre-1995) and 'younger' (post-1995) rehabilitation practices.
Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within the Central Highlands region of south-eastern Australia using both lidar and species distribution models (SDMs). Similarity percentages analysis (SIMPER) was applied to comprehensive floristic surveys to identify five species which best separated stand types. The predicted distributions of these species, modelled using random forests with environmental (i.e., climate, topography) and optical characteristic gradients (Landsat-derived seasonal fractional cover), provided an ecological basis for refining stand type classifications based only on lidar-derived structural profiles. The resulting PEM model represents the first continuous distribution map of stand types across the study region that delineates ecotone stands, which are seral communities comprised of species typical of both rainforest and eucalypt forests. The spatial variability of vegetation structure incorporated into the PEM model suggests that many stand types are not as continuous in cover as represented by current ecological vegetation class distributions that describe the region. Improved PEM models can facilitate sustainable forest management, enhanced forest monitoring, and informed decision making at landscape scales.
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