Tree leaf trade-offs are stronger for sub-canopy trees: leaf traits reveal little about growth rates in canopy trees.

Can morphological plant functional traits predict demographic rates (e.g., growth) within plant communities as diverse as tropical forests? This is one of the most important next-step questions in trait-based ecology and particularly for global reforestation efforts. Due to the diversity of tropical tree species and their longevity, it is difficult to predict their performance prior to reforestation efforts. In this study, we investigate if simple leaf traits are predictors of the more complex ecological process of plant growth in regenerating selectively logged natural forest within the Wet Tropics (WTs) bioregion of Australia. This study used a rich historical data set to quantify tree growth within plots located at Danbulla National Park and State Forest on the Atherton Tableland. Leaf traits were collected from trees that have exhibited fast or slow growth over the last ~50 yr of measurement. Leaf traits were found to be poor predictors of tree growth for trees that have entered the canopy; however, for sub-canopy trees, leaf traits had a stronger association with growth rates. Leaf phosphorus concentrations were the strongest predictor of Periodic Annual Increment (PAI) for trees growing within the sub-canopy, with trees with higher leaf phosphorus levels showing a higher PAI. Sub-canopy tree leaves also exhibited stronger trade-offs between leaf traits and adhere to theoretical predictions more so than for canopy trees. We suggest that, in order for leaf traits to be more applicable to reforestation, size dependence of traits and growth relationships need to be more carefully considered, particularly when reforestation practitioners assign mean trait values to tropical tree species from multiple canopy strata.

The community capacity curve applied to reforestation: a framework to support success.

Community involvement is critical for the success of many interventions designed to promote reforestation. To secure this involvement, it helps to recognize that communities are heterogenous both within and among themselves and possess diverse mixes of livelihood assets required to implement reforestation. We explore the relationship between livelihood assets and reforestation success and outline a conceptual model that we call the community capacity curve (CCC) applied to reforestation. We argue that the shape of the CCC is sigmoidal. Importantly, communities at the lower end of the CCC have limited capacity to implement reforestation projects without substantial and ongoing capacity building and other sorts of support, including through livelihood projects that improve food security and provide cash benefits. Communities at the higher part of the CCC have greater capacity to implement reforestation projects, especially projects focused on biodiversity and environmental services. The CCC can help design, implement, monitor and assess reforestation projects, select appropriate livelihood activities and types of reforestation, select communities suited to a reforestation project, guide implementation and understand projects' successes and failure. The CCC also provides a framework to engage with policy makers and funding bodies to explore the types of support for communities to reforest successfully. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Above-ground biomass recovery following logging and thinning over 46 years in an Australian tropical forest.

Managed tropical forests are a globally important carbon pool, but the effects of logging and thinning intensities on long-term biomass dynamics are poorly known. We investigated the demographic mechanisms of above-ground biomass recovery over 48 years in an Australian tropical forest following four silvicultural treatments: selective logging only as a control and selective logging followed by low-, medium- and high-intensity thinning. Initial biomass recovery rates following thinning were poor predictors of the long-term changes. Initial biomass recovery from 1969 to 1973 was slow and was largely concentrated on an increase in the biomass of residual stems. From 1973 to 1997, above ground biomass (AGB) increased almost linearly, with a similar slope for all sites. From 1997 to 2015, the rate of biomass accumulation slowed, especially for the L treatment. All thinning treatments stimulated more recruitment and regrowth of non-harvested remaining trees compared to the untreated control. Biomass at both the low and medium intensity treatments has almost fully recovered to 98% and 97% of pre-logging biomass levels respectively. The predicted times of complete above-ground biomass recovery for the logging only and high intensity treatments are 55 and 77 years respectively. The slower biomass recovery at the logging only site was largely due to increased mortality in the last measurement period. The slower recovery of the high intensity site was due to a combination of a higher initial reduction in biomass from thinning and the increased mortality in the last measurement period. The high mortality rates in the most recent measurement period are likely due to the impacts of two cyclones that impacted the study site. Our results suggest that it will take at least around 50 years for this site to recover to its pre-harvest biomass, much longer than many of the cutting cycles currently used in tropical forest management.