Relatively, little is known about the relationship between biodiversity and ecosystem functioning in forests, especially in the tropics. We describe the Sabah Biodiversity Experiment: a large-scale, long-term field study on the island of Borneo. The project aims at understanding the relationship between tree species diversity and the functioning of lowland dipterocarp rainforest during restoration following selective logging. The experiment is planned to run for several decades (from seed to adult tree), so here we focus on introducing the project and its experimental design and on assessing initial conditions and the potential for restoration of the structure and functioning of the study system, the Malua Forest Reserve. We estimate residual impacts 22 years after selective logging by comparison with an appropriate neighbouring area of primary forest in Danum Valley of similar conditions. There was no difference in the alpha or beta species diversity of transect plots in the two forest types, probably owing to the selective nature of the logging and potential effects of competitive release. However, despite equal total stem density, forest structure differed as expected with a deficit of large trees and a surfeit of saplings in selectively logged areas. These impacts on structure have the potential to influence ecosystem functioning. In particular, above-ground biomass and carbon pools in selectively logged areas were only 60 per cent of those in the primary forest even after 22 years of recovery. Our results establish the initial conditions for the Sabah Biodiversity Experiment and confirm the potential to accelerate restoration by using enrichment planting of dipterocarps to overcome recruitment limitation. What role dipterocarp diversity plays in restoration only will become clear with long-term results.
South East Asia is widely regarded as a centre of threatened biodiversity owing to extensive logging and forest conversion to agriculture. In particular, forests degraded by repeated rounds of intensive logging are viewed as having little conservation value and are afforded meagre protection from conversion to oil palm. Here, we determine the biological value of such heavily degraded forests by comparing leaf-litter ant communities in unlogged (natural) and twice-logged forests in Sabah, Borneo. We accounted for impacts of logging on habitat heterogeneity by comparing species richness and composition at four nested spatial scales, and examining how species richness was partitioned across the landscape in each habitat. We found that twice-logged forest had fewer species occurrences, lower species richness at small spatial scales and altered species composition compared with natural forests. However, over 80 per cent of species found in unlogged forest were detected within twice-logged forest. Moreover, greater species turnover among sites in twice-logged forest resulted in identical species richness between habitats at the largest spatial scale. While two intensive logging cycles have negative impacts on ant communities, these degraded forests clearly provide important habitat for numerous species and preventing their conversion to oil palm and other crops should be a conservation priority.
This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.
In Amazonian tropical forests, recent studies have reported increases in aboveground biomass and in primary productivity, as well as shifts in plant species composition favouring fast-growing species over slow-growing ones. This pervasive alteration of mature tropical forests was attributed to global environmental change, such as an increase in atmospheric CO2 concentration, nutrient deposition, temperature, drought frequency, and/or irradiance. We used standardized, repeated measurements of over 2 million trees in ten large (16-52 ha each) forest plots on three continents to evaluate the generality of these findings across tropical forests. Aboveground biomass increased at seven of our ten plots, significantly so at four plots, and showed a large decrease at a single plot. Carbon accumulation pooled across sites was significant (+0.24 MgC ha(-1) y(-1), 95% confidence intervals [0.07, 0.39] MgC ha(-1) y(-1)), but lower than reported previously for Amazonia. At three sites for which we had data for multiple census intervals, we found no concerted increase in biomass gain, in conflict with the increased productivity hypothesis. Over all ten plots, the fastest-growing quartile of species gained biomass (+0.33 [0.09, 0.55] % y(-1)) compared with the tree community as a whole (+0.15 % y(-1)); however, this significant trend was due to a single plot. Biomass of slow-growing species increased significantly when calculated over all plots (+0.21 [0.02, 0.37] % y(-1)), and in half of our plots when calculated individually. Our results do not support the hypothesis that fast-growing species are consistently increasing in dominance in tropical tree communities. Instead, they suggest that our plots may be simultaneously recovering from past disturbances and affected by changes in resource availability. More long-term studies are necessary to clarify the contribution of global change to the functioning of tropical forests.
In this paper, we analyze the spatio-temporal distribution of vegetation fires in Peninsular Malaysia, Sumatra, and Borneo in the severe El Niño year of 2015, concentrating on the distribution of fires between mineral soils and peatland areas, and between land cover types in peatland areas. The results reveal that 53% of all Moderate Resolution Imaging Spectroradiometer (MODIS) fire detections were recorded in peatlands that cover only 12% of the study area. However, fire occurrence in the peatland areas was highly dependent on land cover type. Pristine peat swamp forests (PSF) experienced only marginal fire activity (30 fire detections per 1000 km2) compared to deforested undeveloped peatlands (831-915 fire detections per 1000 km2). Our results also highlight the extreme fire vulnerability of the southern Sumatran and Bornean peatlands under strong El Niño conditions: 71% of all peatland hotspots were detected in the provinces of South Sumatra and Central Kalimantan, which contain 29% of peatlands in the study area. Degraded PSF and all deforested peatland land cover types, including managed areas, in the two provinces were severely affected, demonstrating how difficult it is to protect even managed drained agricultural areas from unwanted fires during dry periods. Our results thereby advocate rewetting and rehabilitation as the primary management option for highly fire prone degraded undeveloped peatland areas, whenever feasible, as a means to reduce fire risk during future dry episodes.
The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
The dominant factors controlling soil bacterial community variation within the tropics are poorly known. We sampled soils across a range of land use types--primary (unlogged) and logged forests and crop and pasture lands in Malaysia. PCR-amplified soil DNA for the bacterial 16S rRNA gene targeting the V1-V3 region was pyrosequenced using the 454 Roche machine. We found that land use in itself has a weak but significant effect on the bacterial community composition. However, bacterial community composition and diversity was strongly correlated with soil properties, especially soil pH, total carbon, and C/N ratio. Soil pH was the best predictor of bacterial community composition and diversity across the various land use types, with the highest diversity close to neutral pH values. In addition, variation in phylogenetic structure of dominant lineages (Alphaproteobacteria, Beta/Gammaproteobacteria, Acidobacteria, and Actinobacteria) is also significantly correlated with soil pH. Together, these results confirm the importance of soil pH in structuring soil bacterial communities in Southeast Asia. Our results also suggest that unlike the general diversity pattern found for larger organisms, primary tropical forest is no richer in operational taxonomic units of soil bacteria than logged forest, and agricultural land (crop and pasture) is actually richer than primary forest, partly due to selection of more fertile soils that have higher pH for agriculture and the effects of soil liming raising pH.