Search Results (19)

Short-term observations suggest that environmental changes affect the diversity and composition of soil fungi, significantly influencing forest resilience, plant diversity, and soil processes. However, time-series experiments should be supplemented with geobiological archives to capture the long-term effects of environmental changes on fungi–soil–plant interactions, particularly in undersampled, floristically diverse tropical forests. We recently conducted trnL-P6 amplicon sequencing to generate a sedimentary ancient DNA (sedaDNA) record of the regional catchment vegetation of the tropical waterbody Lake Towuti (Sulawesi, Indonesia), spanning over one million years (Myr) of the lake’s developmental history. In this study, we performed 18SV9 amplicon sequencing to create a parallel paleofungal record to (a) infer the composition, origins, and functional guilds of paleofungal community members and (b) determine the extent to which downcore changes in fungal community composition reflect the late Pleistocene evolution of the Lake Towuti catchment. We identified at least 52 members of Ascomycota (predominantly Dothiodeomycetes, Eurotiomycetes, and Leotiomycetes) and 12 members of Basidiomycota (primarily Agaricales and Polyporales). Spearman correlation analysis of the relative changes in fungal community composition, geochemical parameters, and paleovegetation assemblages revealed that the overwhelming majority consisted of soil organic matter and wood-decaying saprobes, except for a necrotrophic phytopathogenic association between Mycosphaerellaceae (Cadophora) and wetland herbs (Alocasia) in more-than-1-Myr-old silts and peats deposited in a pre-lake landscape, dominated by small rivers, wetlands, and peat swamps. During the lacustrine stage, vegetation that used to grow on ultramafic catchment soils during extended periods of inferred drying showed associations with dark septate endophytes (Ploettnerulaceae and Didymellaceae) that can produce large quantities of siderophores to solubilize mineral-bound ferrous iron, releasing bioavailable ferrous iron needed for several processes in plants, including photosynthesis. Our study showed that sedaDNA metabarcoding paired with the analysis of geochemical parameters yielded plausible insights into fungal-plant-soil interactions, and inferred changes in the paleohydrology and catchment evolution of tropical Lake Towuti, spanning more than one Myr of deposition. Full article

Mutualisms characterized by reciprocal benefits between species are a fundamental relationship of tropical ecosystems. Ant Gardens (AGs) represent an interesting ant-plant mutualism, involving specialized interactions between vascular epiphytes and ants. While this relationship has been extensively studied in various tropical regions, the available information on Peruvian ecosystems is limited. The objective of this study was to identify the ant and epiphyte species that constitute AGs. From February 2023 to January 2024, a study was conducted on two 50 × 10 m transects within the Mauritia flexuosa peat swamp forest, located within the Water Association Aguajal Renacal del Alto Mayo (ADECARAM) Tingana in San Martín, Peru. A total of 69 ant gardens were documented, comprising 18 phorophyte species, 19 epiphyte species, and three ant species. The results demonstrated that neither the height nor the diameter at breast height (DBH) of phorophytes exhibited a statistically significant correlation with the number of AGs per host. However, a positive correlation was observed between the length and width of the AGs and the number of ants per AG. The findings of this study contribute to the understanding of AG mutualism in Peruvian ecosystems. Full article

Climate change poses significant challenges to ecosystems globally, demanding innovative methods for environmental conservation and restoration. Restoration initiatives require significant amounts of appropriate vegetation that is both adaptive and tolerant to the specific environmental factors. This study introduces an adaptive-vegetation model designed to support ecosystem resilience in the face of climate change. Traditional restoration methods often neglect dynamic environmental conditions and ecosystem interactions, but the model employs real-time data and predictive analytics to adapt strategies to evolving climate variables. The model takes a comprehensive approach, incorporating climate projections, soil health metrics, species adaptability, and hydrological patterns to inform restoration practices. By using a mix of adaptable native species, the model promotes biodiversity. In conclusion, according to the findings of our review, paludiculture and agroforestry could be implemented as models for improving climate resilience, particularly in tropical degraded peat swamp forests. These two models could improve the environment, the economy, and social functions. Finally, improving all three of these factors improves ecological stability. This adaptive-vegetation model represents a significant shift from static, uniform restoration approaches to dynamic, data-driven strategies tailored to specific environments. The future research directions underscore the need for ongoing innovation in conservation practices to safeguard ecosystems amid unprecedented environmental changes. Future efforts will focus on enhancing the model with advanced machine learning techniques and expanding its application to additional ecological contexts. Full article

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a ‘LiDAR AGB model’, to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R² = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology. Full article

Draining deforested tropical peat swamp forests (PSFs) converts greenhouse gas (GHG) sinks to sources and increases the likelihood of fire hazards. Rewetting deforested and drained PSFs before revegetation is expected to reverse this outcome. This study aims to quantify the GHG emissions of deforested PSFs that have been (a) reforested, (b) converted into oil palm, or (c) replanted with rubber. Before rewetting, heterotrophic soil respiration in reforested, oil palm, and rubber plantation areas were 48.91 ± 4.75 Mg CO₂ ha⁻¹ yr⁻¹, 54.98 ± 1.53 Mg CO₂ ha⁻¹ yr⁻¹, and 67.67 ± 2.13 Mg CO₂ ha⁻¹ yr⁻¹, respectively. After rewetting, this decreased substantially by 21%, 36%, and 39%. Conversely, rewetting drained landscapes that used to be methane (CH₄) sinks converted them into CH₄ sources; almost twice as much methane was emitted after rewetting. Nitrous oxide (N₂O) emissions tended to decrease; in nitrogen-rich rubber plantations, N₂O emissions halved; in nitrogen-poor reforested areas, emissions reduced by up to a quarter after rewetting. Overall, rewetting reduced the net emissions up to 15.41 Mg CO₂-eq ha⁻¹ yr⁻¹ (25%) in reforested, 18.36 Mg CO₂-eq ha⁻¹ yr⁻¹ (18%) in oil palm, and 28.87 Mg CO₂-eq ha⁻¹ yr⁻¹ (17%) in rubber plantation areas. Full article

Degraded and drained peat swamp forests (PSFs) are major sources of carbon emissions in the forestry sector. Rewetting interventions aim to reduce carbon loss and to enhance the carbon stock. However, studies of rewetting interventions in tropical PSFs are still limited. This study examined the effect of rewetting interventions on carbon dynamics at a rewetted site and an undrained site. We measured aboveground carbon (AGC), belowground carbon (BGC), litterfall, heterotrophic components of soil respiration (R_h), methane emissions (CH₄), and dissolved organic carbon (DOC) concentration at both sites. We found that the total carbon stock at the rewetted site was slightly lower than at the undrained site (1886.73 ± 87.69 and 2106.23 ± 214.33 Mg C ha⁻¹, respectively). The soil organic carbon (SOC) was 1685 ± 61 Mg C ha⁻¹ and 1912 ± 190 Mg C ha⁻¹ at the rewetted and undrained sites, respectively, and the carbon from litterfall was 4.68 ± 0.30 and 3.92 ± 0.34 Mg C ha⁻¹ year⁻¹, respectively. The annual average R_h was 4.06 ± 0.02 Mg C ha⁻¹ year⁻¹ at the rewetted site and was 3.96 ± 0.16 Mg C ha⁻¹ year⁻¹ at the undrained site. In contrast, the annual average CH₄ emissions were −0.0015 ± 0.00 Mg C ha⁻¹ year⁻¹ at the rewetted site and 0.056 ± 0.000 Mg C ha⁻¹ year⁻¹ at the undrained site. In the rewetted condition, carbon from litter may become stable over a longer period. Consequently, carbon loss and gain mainly depend on the magnitude of peat decomposition (R_h) and CH₄ emissions. Full article

Tropical peat swamp forests, found mainly in Southeast Asia, have been threatened by recurring El Niño fires. Repeated burnings form a complex and heterogeneous landscape comprising a mosaic of burned patches of different fire frequencies, requiring fine-scale assessment to understand their impact. We examined the impact of the El Niño fires of 1998 and 2003 on a tropical peat swamp forest in northern Borneo, with the combined use of high and very high-resolution satellite images. Object-based and pixel-based classifications were compared to classify a QuickBird image. Burned patches of different fire frequencies were derived based on unsupervised classification of the principal components of multitemporal Normalized Difference Water Index (NDWI) data. The results show that the object-based classification was more accurate than the pixel-based classification for generating a detailed land cover map. Fire frequency had a severe impact on the number of burned patches and the residual forest cover. Larger patch area retained more residual forest cover for the burned patches. Forest structure of burned-twice patches was more severely altered compared to burned-once patches. Two burned-once patches had a relatively promising recovery potential by natural regeneration due to higher residual forest cover, a vast number of large trees, and aboveground biomass. Except for the largest patch, rehabilitation seemed inevitable for burned-twice patches. This approach can be applied to assess the impact of multiple fires on other forest types for better post-fire forest management. Full article

Deforested and converted tropical peat swamp forests are susceptible to fires and are a major source of greenhouse gas (GHG) emissions. However, information on the influence of land-use change (LUC) on the carbon dynamics in these disturbed peat forests is limited. This study aimed to quantify soil respiration (heterotrophic and autotrophic), net primary production (NPP), and net ecosystem production (NEP) in peat swamp forests, partially logged forests, early seral grasslands (deforested peat), and smallholder-oil palm estates (converted peat). Peat swamp forests (PSF) showed similar soil respiration with logged forests (LPSF) and oil palm (OP) estates (37.7 Mg CO₂ ha⁻¹ yr⁻¹, 40.7 Mg CO₂ ha⁻¹ yr⁻¹, and 38.7 Mg CO₂ ha⁻¹ yr⁻¹, respectively), but higher than early seral (ES) grassland sites (30.7 Mg CO₂ ha⁻¹ yr⁻¹). NPP of intact peat forests (13.2 Mg C ha⁻¹ yr⁻¹) was significantly greater than LPSF (11.1 Mg C ha⁻¹ yr⁻¹), ES (10.8 Mg C ha⁻¹ yr⁻¹), and OP (3.7 Mg C ha⁻¹ yr⁻¹). Peat swamp forests and seral grasslands were net carbon sinks (10.8 Mg CO₂ ha⁻¹ yr⁻¹ and 9.1 CO₂ ha⁻¹ yr⁻¹, respectively). In contrast, logged forests and oil palm estates were net carbon sources; they had negative mean Net Ecosystem Production (NEP) values (−0.1 Mg CO₂ ha⁻¹ yr⁻¹ and −25.1 Mg CO₂ ha⁻¹ yr⁻¹, respectively). The shift from carbon sinks to sources associated with land-use change was principally due to a decreased Net Primary Production (NPP) rather than increased soil respiration. Conservation of the remaining peat swamp forests and rehabilitation of deforested peatlands are crucial in GHG emission reduction programs. Full article

The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge. Full article

Palm oil production is a key industry in tropical regions, driven by the demand for affordable vegetable oil. Palm oil production has been increasing by 9% every year, mostly due to expanding biofuel markets. However, the oil palm industry has been associated with key environmental issues, such as deforestation, peatland exploitation and biomass burning that release carbon dioxide (CO₂) into the atmosphere, leading to climate change. This review therefore aims to discuss the characteristics of oil palm plantations and their impacts, especially CO₂ emissions in the Southeast Asian region. The tropical climate and soil in Southeast Asian countries, such as Malaysia and Indonesia, are very suitable for growing oil palm trees. However, due to the scarcity of available plantation areas deforestation occurs, especially in peat swamp areas. Total carbon losses from both biomass and peat due to the conversion of tropical virgin peat swamp forest into oil palm plantations are estimated to be around 427.2 ± 90.7 t C ha⁻¹ and 17.1 ± 3.6 t C ha⁻¹ year⁻¹, respectively. Even though measured CO₂ fluxes have shown that overall, oil palm plantation CO₂ emissions are about one to two times higher than other major crops, the ability of oil palms to absorb CO₂ (a net of 64 tons of CO₂ per hectare each year) and produce around 18 tons of oxygen per hectare per year is one of the main advantages of this crop. Since the oil palm industry plays a crucial role in the socio-economic development of Southeast Asian countries, sustainable and environmentally friendly practices would provide economic benefits while minimizing environmental impacts. A comprehensive review of all existing oil plantation procedures is needed to ensure that this high yielding crop has highly competitive environmental benefits. Full article

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha ${}^{-1}$ with a standard error of 11.17 Mgha ${}^{-1}$ (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha ${}^{-1}$ with a slightly smaller standard error of 10.57 Mgha ${}^{-1}$ (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha ${}^{-1}$ . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision. Full article

Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R² = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed. Full article

Tree growth rings are signs of the seasonality of tree growth and indicate how tree productivity relates to environmental factors. We studied the periodicity of tree growth ring formation in seasonally inundated peatlands of Central Kalimantan (southern Borneo), Indonesia. We collected samples from 47 individuals encompassing 27 tree species. About 40% of these species form distinct growth zones, 30% form indistinct ones, and the others were classified as in between. Radiocarbon age datings of single distinct growth zones (or “rings”) of two species showing very distinct rings, Horsfieldia crassifolia and Diospyros evena, confirm annual growth periodicity for the former; the latter forms rings in intervals of more than one year. The differences can be explained with species-specific sensitivity to the variable intensity of dry periods. The anatomical feature behind annual rings in Horsfieldia is the formation of marginal parenchyma bands. Tree ring curves of other investigated species with the same anatomical feature from the site show a good congruence with the curves from H. crassifolia. They can therefore be used as indicator species for growth rate estimations in environments with weak seasonality. The investigated peatland species show low annual growth increments compared to other tropical forests. Full article

We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C∙ha⁻¹ for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR—albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires. Full article

In Southeast Asia land use change associated with forest loss and degradation is a major source of greenhouse gas (GHG) emissions. This is of particular concern where deforestation occurs on peat soils. A business-as-usual (BAU) land change model was developed using Dinamica EGO© for a REDD+ Demonstration Activity area in south-east Jambi Province, Sumatra, Indonesia containing Berbak National Park (NP). The model output will be used as baseline land change predictions for comparison with alternative land cover management scenarios as part of a REDD+ feasibility study. The study area is approximately 376,000 ha with approximately 50% on peat soils. The model uses published 2000 and 2010 land cover maps as input and projects land cover change for thirty years until 2040. The model predicted that under a BAU scenario the forest area, 185,000 ha in 2010, will decline by 37% by 2040. In protected forest areas, approximately 50% of the study area, forest cover will reduce by 25%. Peat swamp forest will reduce by almost 37%. The greatest land cover category increases are plantation/regrowth areas (which includes oil palm) and open areas which each increase by 30,000 ha. These results indicate that the site has great potential as an Indonesian REDD+ Demonstration Activity. Full article