Sustainable forest management (SFM) practices have started in 1999 in Turkey. A set of criteria and indicators, composed by the General Directorate of Forestry (GDF) on the basis of the criteria and indicators defined in the Pan-European and Near Eastern Processes, was enquired via a survey to serve this purpose. GDF tested the sustainability under the following titles: Situation of forest resources, biodiversity, health and vitality, production capacity and functions, protective functions and environmental and socio-economic functions. There were problems in identification and definition of SFM criteria and indicators. Biological diversity indicators has been selected, described and developed in this study. At this phase, the survey was completed upon receiving the views of the scientists interested in different dimensions of this topic as well as the views of other interest groups affiliated with forestry. As a result, there were 13 indicators that may be used as the basis of a regional or forest management unit level for the purpose of protecting, developing and maintaining biodiversity. Furthermore, these indicators are instruments, which may easily be used by relevant decision-makers in the management of forest resources in a more effective and productive manner.
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.
The complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes.
Humans influence tropical rainforest animals directly via exploitation and indirectly via habitat disturbance. Bushmeat hunting and logging occur extensively in tropical forests and have large effects on particular species. But how they alter animal diversity across landscape scales and whether their impacts are correlated across species remain less known. We used spatially widespread measurements of mammal occurrence across Malaysian Borneo and recently developed multispecies hierarchical models to assess the species richness of medium- to large-bodied terrestrial mammals while accounting for imperfect detection of all species. Hunting was associated with 31% lower species richness. Moreover, hunting remained high even where richness was very low, highlighting that hunting pressure persisted even in chronically overhunted areas. Newly logged sites had 11% lower species richness than unlogged sites, but sites logged >10 years previously had richness levels similar to those in old-growth forest. Hunting was a more serious long-term threat than logging for 91% of primate and ungulate species. Hunting and logging impacts across species were not correlated across taxa. Negative impacts of hunting were the greatest for common mammalian species, but commonness versus rarity was not related to species-specific impacts of logging. Direct human impacts appeared highly persistent and lead to defaunation of certain areas. These impacts were particularly severe for species of ecological importance as seed dispersers and herbivores. Indirect impacts were also strong but appeared to attenuate more rapidly than previously thought. The lack of correlation between direct and indirect impacts across species highlights that multifaceted conservation strategies may be needed for mammal conservation in tropical rainforests, Earth's most biodiverse ecosystems.
Selective logging and forest conversion to oil palm agriculture are rapidly altering tropical forests. However, functional responses of the soil microbiome to these land-use changes are poorly understood. Using 16S rRNA gene and shotgun metagenomic sequencing, we compared composition and functional attributes of soil biota between unlogged, once-logged and twice-logged rainforest, and areas converted to oil palm plantations in Sabah, Borneo. Although there was no significant effect of logging history, we found a significant difference between the taxonomic and functional composition of both primary and logged forests and oil palm. Oil palm had greater abundances of genes associated with DNA, RNA, protein metabolism and other core metabolic functions, but conversely, lower abundance of genes associated with secondary metabolism and cell-cell interactions, indicating less importance of antagonism or mutualism in the more oligotrophic oil palm environment. Overall, these results show a striking difference in taxonomic composition and functional gene diversity of soil microorganisms between oil palm and forest, but no significant difference between primary forest and forest areas with differing logging history. This reinforces the view that logged forest retains most features and functions of the original soil community. However, networks based on strong correlations between taxonomy and functions showed that network complexity is unexpectedly increased due to both logging and oil palm agriculture, which suggests a pervasive effect of both land-use changes on the interaction of soil microbes.
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.
Genetically modified organisms (GMO) are increased remarkably from year to year and the estimated global area cultivated with genetically modified (GM) crops reached 125 million hectares in year 2008. However, insect resistance maize based on Bacillus thuringienses (Bt) is of the most cultivated GM crop in worldwide. Bacillus thuringiensis (Bt) is an aerobic, gram-positive bacterium that synthesize one or more Cry protein that are toxic to various types crop and forestry insects pests. To date, several cry genes have been introduced into GM plant to combat with various type of insect. Worldwide commercialization of GM crops has raised the customers’ concern about the Biosafety issues, and thus, many countries have implemented the labeling legislations for GM food and their derivatives. In this study, we introduced the quantitative analysis method based on the recombinant plasmid DNA as calibrators that can be used to determine the percentage of GMO content in various types of food and feed samples. Therefore, we have reported 7.5% (6/80) of the samples were contained StarLink maize and 1.25% (1/80) samples were contained Bt176 maize. Additionally, the percentage of GM content in each positive sample were further determined with the developed quantitative method. The percentage of the StarLink corns that present in the positive samples were varies from 0.09% to 2.53% and Bt176 corn that present in the positive sample was 16.90%. The present study demonstrated that the recombinant plasmid DNA that used in quantitative real-time method as good alternative quantitative analysis of GM content.