Leaf samples of tropical trees, i.e. Dryobalanops lanceolata (Kapur paji), Dipterocarpaceae and Macaranga spp. (Mahang), Euphorbiaceae were analyzed for 21 chemical elements. The pioneer Macaranga spp. exhibited higher concentrations for the majority of elements compared to the emergent species of Dryobalanops lanceolata, which was attributed to the higher physiological activity of the fast growing pioneer species compared to emergent trees. Lead showed rather high concentrations in several samples from the Bakam re-forestation site. This is suggested to be caused by emissions through brick manufacturing and related activities in the vicinity. A comparison of Dryobalanops lanceolata samples collected in 1993, 1995 and 1997 in the Lambir Hills National Park revealed that certain heavy metals, i.e. Co, Cu, Mn, Ni, Pb and Ti showed higher values in 1997 compared to the previous years, which could indicate an atmospheric input from the haze caused by the extensive forest fires raging in Borneo and other parts of Southeast Asia.
The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ∼364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km(2) of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests.
Tree autotrophic respiratory processes, especially stem respiration or stem CO2 efflux (Estem), are important components of the forest carbon budget. Despite efforts to investigate the controlling processes of Estem in recent years, a considerable lack in our knowledge remains on the abiotic and biotic drivers affecting Estem dynamics. It has been strongly advocated that long-term measurements would shed light onto those processes. The expensive scientific instruments needed to measure gas exchange have prevented Estem measurements from being applied on a larger temporal and spatial scale. Here, we present an automated closed dynamic chamber system based on inexpensive and industrially broadly applied CO2 sensors, reducing the costs for the sensing system to a minimum. The CO2 sensor was cross-calibrated with a commonly used gas exchange system in the laboratory and in the field, and we found very good accordance of these sensors. We tested the system under harsh tropical climatic conditions, characterized by heavy tropical rainfall events, extreme humidity and temperatures, in a moist lowland forest in Malaysia. We recorded Estem of three Dyera costulata (Miq.) trees with our prototype over various days. The variation of Estem was large among the three tree individuals and varied by 7.5-fold. However, clear diurnal changes in Estem were present in all three tree individuals. One tree showed high diurnal variation in Estem, and the relationship between Estem and temperature was characterized by a strong hysteresis. The large variations found within one single tree species highlight the importance of continuous measurement to quantify ecosystem carbon fluxes.
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
Sympatric gibbon species Hylobates lar and H. syndactylus were censused on a mountain in Malaya (West Malaysia). Habitat quality was assessed between 380- and 1,525-m altitudes. H. syndactylus was found to occur up to altitudes higher than does H. lar, and this is discussed with reference to the two species' divergent foraging strategies indicated by previous research. It is suggested that gibbons are restricted in their altitudinal range by an increasingly unfavourable ratio of food consumed to energy expended in its location, caused by a reduced food-source density and more difficult terrain at higher elevations.
The effects of commercial logging on tree diversity in tropical rainforest are largely unknown. In this study, selectively logged tropical rainforest in Indonesian Borneo is shown to contain high tree species richness, despite severe structural damage. Plots logged 8 years before sampling contained fewer species of trees greater than 20 centimeters in diameter than did similar-sized unlogged plots. However, in samples of the same numbers of trees (requiring a 50 percent larger area), logged forest contained as many tree species as unlogged forest. These findings warrant reassessment of the conservation potential of large tracts of commercially logged tropical rainforest.
The globalization of the palm oil trade poses a menace to the ecosystem integrity of Southeast Asia. In this short communication, we briefly discuss why palm oil certifications may have failed as an effective means to halt forest degradation and biodiversity loss. From a comparison of multiple new datasets, we analysed recent tree loss in Indonesia, Malaysia, and Papua New Guinea, and discovered that, from 2001 to 2016, about 40% of the area located in certified concessions suffered from habitat degradation, deforestation, fires, or other tree damages. Certified concessions have been subject to more tree removals than non-certified ones. We also detect significant tree loss before and after the start of certification schemes. Beyond non-governmental organisations' concern that Roundtable on Sustainable Palm Oil (RSPO) and Palm Oil Innovation Group (POIG) certifications allow ongoing clearance of any forest not identified as of high conservation values (HCV) or high carbon stock (HCS), we suggest an alarming and previously overlooked situation, such as that current "sustainable palm oil" is often associated with recent habitat degradation and forest loss. In other words, certified palm oil production may not be so sustainable.
Logging and poor shifting cultivation negatively affect initial soil carbon (C) storage, especially at the initial stage of deforestation, as these practices lead to global warming. As a result, an afforestation program is needed to mitigate this problem. This study assessed initial soil C buildup of rehabilitated forests using Fourier transform infrared (FTIR) spectroscopy. The relatively high E4/E6 values of humic acids (HAs) in the rehabilitated forest indicate prominence of aliphatic components, suggesting that the HAs were of low molecular weight. The total acidity, carboxylic (-COOH) and phenolic (-OH) of the rehabilitated forest were found to be consistent with the ranges reported by other researchers. The spectra of all locations were similar because there was no significant difference in the quantities of C in humic acids (CHA) regardless of forest age and soil depth. The spectra showed distinct absorbance at 3290, 1720, 1630, 1510, 1460, 1380, and 1270 cm-1. Increase of band at 1630 and 1510 cm-1 from 0-20 to 40-60 cm were observed, suggesting C buildup from the lowest depths 20-40 and 40-60 cm. However, the CHA content in the soil depths was not different. The band at 1630 cm-1 was assigned to carboxylic and aromatic groups. Increase in peak intensity at 1510 cm-1 was because C/N ratio increased with increasing soil depth. This indicates that decomposition rate decreased with increasing soil depth and decreased with CHA. The finding suggests that FTIR spectroscopy enables the assessment of C composition functional group buildup at different depths and ages.
A bacterial strain, KM1S, was isolated from a Malaysian rainforest soil sample by using a defined enrichment medium that specifically facilitates selection of quorum quenching bacteria. KM1S was clustered closely to Bacillus cereus by 16S ribosomal DNA sequence analysis. It degraded N-3-oxo-hexanoyl homoserine lactone and N-3-oxo-octanoyl homoserine lactone in vitro rapidly at 4.98 and 6.56 microg AHL h(-1) per 10(9) CFU/ml, respectively, as determined by the Rapid Resolution Liquid Chromatography. The aiiA homologue, encoding an autoinducer inactivation enzyme catalyzing the degradation of N-acylhomoserine lactones, of KM1S was amplified and cloned. Sequence analysis indicated the presence of the motif (106)HXDH-59 amino acids-H(169)-21 amino acids-D(191) for N-acylhomoserine lactone lactonases.
Background. Two non-tuberculous mycobacterial strains, UM_3 and UM_11, were isolated from the trunk wash of captive elephants in Malaysia. As they appeared to be identical phenotypes, they were investigated further by conventional and whole genome sequence-based methods of strain differentiation. Methods. Multiphasic investigations on the isolates included species identification with hsp65 PCR-sequencing, conventional biochemical tests, rapid biochemical profiling using API strips and the Biolog Phenotype Microarray analysis, protein profiling with liquid chromatography-mass spectrometry, repetitive sequence-based PCR typing and whole genome sequencing followed by phylogenomic analyses. Results. The isolates were shown to be possibly novel slow-growing schotochromogens with highly similar biological and genotypic characteristics. Both strains have a genome size of 5.2 Mbp, G+C content of 68.8%, one rRNA operon and 52 tRNAs each. They qualified for classification into the same species with their average nucleotide identity of 99.98% and tetranucleotide correlation coefficient of 0.99999. At the subspecies level, both strains showed 98.8% band similarity in the Diversilab automated repetitive sequence-based PCR typing system, 96.2% similarity in protein profiles obtained by liquid chromatography mass spectrometry, and a genomic distance that is close to zero in the phylogenomic tree constructed with conserved orthologs. Detailed epidemiological tracking revealed that the elephants shared a common habitat eight years apart, thus, strengthening the possibility of a clonal relationship between the two strains.
Entomological investigations on malaria and bancroftian filariasis transmission were carried out in the endemic area of Baram District, Sarawak. The Anopheles composition, survival and infection rates of malaria and filariasis were compared in the village and 0.5 km from the village ecotype, in forested areas. Anopheles leucosphyrus, An. barbirostris and An. donaldi are the vectors for malaria and bancroftian filariasis in both ecotypes. Biting and infection rates vary, but An. leucosphyrus differed with a peak around midnight in the forested area and soon after dusk in the village setting. The parous rate of An. leucosphyrus was significantly higher in the forest ecotype (P < 0.0001); however, the proportion of 3-parous and older was not overall higher in the forest ecotype (P = 0.68). The entomological inoculation of malaria parasites by An. leucosphyrus was comparatively higher in the forested areas (P > 0.5). The implications of malaria and filariasis transmission in the forested areas in Baram District are discussed.
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.
The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
Long-term observations are presented on the behaviour of the siamang ape, Symphalangus syndactylus, in the lowland forest of central Malaya. The data were collected during two dry and three fruiting seasons between 1969 and 1973 inclusive on two groups with adjacent ranges; comparisons are made within and between sample periods, and between groups. The influence of weather on daily activities is considered. Food intake is analysed in terms of number of food trees, number of visits to these trees, and the cumulative time spent feeding on various food categories. Ranging behaviour is investigated in terms of distance travelled, area covered, and distribution of time and of food trees about the range. The occurrence of calling is described and compared with that of the white-handed gibbon in the same area. A discussion ensues on each of these aspects of behaviour in turn. Emphasis is laid on the similarity of behaviour of the two groups at any one time, and on the degree of their response to the fluctuations of environment variables. Finally, the application to siamang of ranging concepts currently used in animal behaviour is considered briefly.
We report the production and degradation of quorum sensing N-acyl-homoserine lactones by bacteria isolated from Malaysian montane forest soil. Phylogenetic analysis indicated that these isolates clustered closely to the genera of Arthrobacter, Bacillus and Pseudomonas. Quorum quenching activity was detected in six isolates of these three genera by using a series of bioassays and rapid resolution liquid chromatography analysis. Biosensor screening and high resolution liquid chromatography-mass spectrometry analysis revealed the production of N-dodecanoyl-L-homoserine lactone (C12-HSL) by Pseudomonas frederiksbergensis (isolate BT9). In addition to degradation of a wide range of N-acyl-homoserine lactones, Arthrobacter and Pseudomonas spp. also degraded p-coumaroyl-homoserine lactone. To the best of our knowledge, this is the first documentation of Arthrobacter and Pseudomonas spp. capable of degrading p-coumaroyl-homoserine lactone and the production of C12-HSL by P. frederiksbergensis.
Climate is widely recognised as an important determinant of the latitudinal diversity gradient. However, most existing studies make no distinction between direct and indirect effects of climate, which substantially hinders our understanding of how climate constrains biodiversity globally. Using data from 35 large forest plots, we test hypothesised relationships amongst climate, topography, forest structural attributes (stem abundance, tree size variation and stand basal area) and tree species richness to better understand drivers of latitudinal tree diversity patterns. Climate influences tree richness both directly, with more species in warm, moist, aseasonal climates and indirectly, with more species at higher stem abundance. These results imply direct limitation of species diversity by climatic stress and more rapid (co-)evolution and narrower niche partitioning in warm climates. They also support the idea that increased numbers of individuals associated with high primary productivity are partitioned to support a greater number of species.
Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.