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'.

The road to recovery: a synthesis of outcomes from ecosystem restoration in tropical and sub-tropical Asian forests.

Current policy is driving renewed impetus to restore forests to return ecological function, protect species, sequester carbon and secure livelihoods. Here we assess the contribution of tree planting to ecosystem restoration in tropical and sub-tropical Asia; we synthesize evidence on mortality and growth of planted trees at 176 sites and assess structural and biodiversity recovery of co-located actively restored and naturally regenerating forest plots. Mean mortality of planted trees was 18% 1 year after planting, increasing to 44% after 5 years. Mortality varied strongly by site and was typically ca 20% higher in open areas than degraded forest, with height at planting positively affecting survival. Size-standardized growth rates were negatively related to species-level wood density in degraded forest and plantations enrichment settings. Based on community-level data from 11 landscapes, active restoration resulted in faster accumulation of tree basal area and structural properties were closer to old-growth reference sites, relative to natural regeneration, but tree species richness did not differ. High variability in outcomes across sites indicates that planting for restoration is potentially rewarding but risky and context-dependent. Restoration projects must prepare for and manage commonly occurring challenges and align with efforts to protect and reconnect remaining forest areas. The abstract of this article is available in Bahasa Indonesia in the electronic supplementary material. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands.

Tropical peatlands play a vital role in the global carbon cycle as large carbon reservoirs and substantial carbon sinks. Indonesia possesses the largest share (65 %) of tropical peat carbon, equal to 57.4 Gt C. Human perturbations such as extensive logging, deforestation and canalization exacerbate water losses, especially during dry seasons, when low precipitation and high evapotranspiration rates combine with the increased drainage to lower groundwater levels. Drying and increasing temperatures of the surface peat exacerbate ignition and wildfire risks within the peat soils. As such, it is critically important to know how groundwater levels in peatlands are changing over space and time. In this study, a multilinear regression model as well as two machine learning algorithms, random forest and extreme gradient boosting, were used to model groundwater level over the study period (2010-12) within a peat dome impacted by drainage canals and multiple wildfires in Central Kalimantan, Indonesia. Although all three models performed well, based on overall fit, spatial modeling of groundwater level results revealed that extreme gradient boosting (R2 = 0.998, RMSE = 0.048 m) outperformed random forest (R2 = 0.997, RMSE = 0.054 m) and multilinear regression (R2 = 0.970, RMSE = 0.221 m) near drainage canals, which are key fire ignition risk locations in the peatlands. Our study also shows that, on average, elevation and precipitation are the most important parameters influencing groundwater level spatiotemporally.

Application of agroforestry business models to tropical peatland restoration.

Indonesia is home to around 45% of the world's tropical peatlands which continue to be degraded on a large scale by deforestation, drainage and fire, contributing massively to global GHG emissions. Approaches to restoring the peat-water balance and reducing emissions in peat hydrological units, through managing them based either on full protection or large-scale commercial production, have generally failed to address environmental and local community needs. We present published and unpublished findings pointing to the need for an integrated peatland protection and restoration strategy based first on raising water levels in degraded (drained) peatlands and maintaining them in forested peatlands, thus, reducing GHG emissions. Second, the strategy incorporates ecologically sound agroforestry business models that strengthen livelihoods of smallholders and so sustain their interest in sustainably managing the peatlands. In this paper, we focus on the second element of this strategy in Indonesia. Eight agroforestry business models are proposed based on their merits to attract both smallholders and commercial investors as well as their compatibility with hydrological rehabilitation of the peatlands. While financial returns on investment will vary across sites and countries, our analysis indicates that some models can be profitable over both short and longer time periods with relatively low levels of investment risk.

Field methods for above and belowground biomass estimation in plantation forests.

A practical and cost-effective destructive sampling method for estimating above and belowground biomass of Corymbia citriodora subspecies variegata grown in plantations is described. The methodology includes details on selecting sample trees, weighing tree components in the field, excavating root systems and obtaining fresh weights and laboratory analyses of components to determine oven dry weights. The development of these sampling procedures is a basic step towards successful, consistent collection of biomass data in 18-20 years old plantation forests. This methodology was developed for eucalypt hardwood timber plantations in Queensland, Australia. However, these procedures can be applied to plantations elsewhere as well as to trees in native forest environments with minor modifications. The methodology developed for field sampling of the tree components and the derivation of allometric relationships for predicting individual tree biomass (above and belowground) highlighted the following:•Accurate quantification of above and belowground biomass of eucalypts.•Description of measured variables for developing allometric relationships.•Integration of field and laboratory measurements to streamline data collection.

Effects of distance from canal and degradation history on peat bulk density in a degraded tropical peatland.

Over recent decades, the combination of deforestation, peat drainage and fires have resulted in widespread degradation of Southeast Asia's tropical peatlands. These disturbances are generally thought to increase peat soil bulk density through peat drying and shrinkage, compaction, and consolidation. Biological oxidation and fires burning across these landscapes also consume surface peat, exposing older peat strata. The prevalence and severity of deforestation, peat drainage and fire are typically greater closer to canals, built to drain peatlands and provide access routes for people. We compared bulk densities of 240cm peat profiles from intact forests and degraded peatlands broadly, and also assessed differences between degraded peatlands near-to-canals (50-200m from the nearest canal) and far-from-canals (300+ m from the nearest canal). The effects of vegetation type and fire frequency on bulk density, irrespective of the distance from canal, were also investigated. Mean bulk density values ranged between 0.08 and 0.16gcm-3 throughout the 240cm peat profiles. Drainage of peat near-to-canals increased bulk density of peat above the minimum water table depth. Degradation by deforestation and fire also increased bulk densities of upper peat strata, albeit with greater variability. Peat sampled further from canals experienced less intense water table drawdowns, buffering them from drainage effects. These areas were also more commonly forested and burnt less frequently. Differences in bulk densities below minimum water table levels are less clear, but may reflect lowering of the current peat surface in degraded peatlands broadly. These results clearly show that important differences in bulk density exist across degraded peatlands that are spatially dependent on distance from canals and disturbance history. These landscape features should be taken into account when designing future bulk density sampling efforts and peatland restoration programs, or when extrapolating from existing sources in order to make accurate inferences from them.

Without management interventions, endemic wet-sclerophyll forest is transitioning to rainforest in World Heritage listed K'gari (Fraser Island), Australia.

Wet-sclerophyll forests are unique ecosystems that can transition to dry-sclerophyll forests or to rainforests. Understanding of the dynamics of these forests for conservation is limited. We evaluated the long-term succession of wet-sclerophyll forest on World Heritage listed K'gari (Fraser Island)-the world's largest sand island. We recorded the presence and growth of tree species in three 0.4 hectare plots that had been subjected to selective logging, fire, and cyclone disturbance over 65 years, from 1952 to 2017. Irrespective of disturbance regimes, which varied between plots, rainforest trees recruited at much faster rates than the dominant wet-sclerophyll forest trees, narrowly endemic species Syncarpia hillii and more common Lophostemon confertus. Syncarpia hillii did not recruit at the plot with the least disturbance and recruited only in low numbers at plots with more prominent disturbance regimes in the ≥10 cm at breast height size. Lophostemon confertus recruited at all plots but in much lower numbers than rainforest trees. Only five L. confertus were detected in the smallest size class (<10 cm diameter) in the 2017 survey. Overall, we find evidence that more pronounced disturbance regimes than those that have occurred over the past 65 years may be required to conserve this wet-sclerophyll forest, as without intervention, transition to rainforest is a likely trajectory. Fire and other management tools should therefore be explored, in collaboration with Indigenous landowners, to ensure conservation of this wet-sclerophyll forest.

Testing the generality of above-ground biomass allometry across plant functional types at the continent scale.

Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha(-1) ). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures).