Detection of potential harmful algal bloom-causing microalgae from freshwater prawn farms in Central Luzon,Philippines, for bloom monitoring and prediction

Prawn aquaculture industry is one of the developing economic activities in the Philippines. Generally, a wide range of microalgae typically grow and proliferate in many aquaculture ponds. However, certain species of microalgae have the ability to form harmful algal blooms (HABs) which often bring damaging consequences to the aquaculture industry, food safety, and the environment. The study aims to identify and characterize the composition of microalgae (particularly Cyanophyta) that are capable of forming HABs in selected freshwater prawn culture ponds in Central Luzon, Philippines, using morphological, ultrastructural, and molecular characterizations. From water samples collected in ten selected ponds across Central Luzon (Bulacan, Nueva Ecija, Pampanga, Tarlac, and Zambales), potential HAB formers such as Oscillatoria agardhii, O. princeps, Microcystis aeruginosa, and M. wesenbergii were observed to have occurred mostly, while Anabaena circinalis was only observed in one site. Both morphological and ultrastructural methods brought upon some challenges and limitations while molecular methods using 16S rRNA gene and phylogenetic analysis which were optimized in this study offered better and more efficient ways of identification and were helpful in resolving genus-level and species-level relationships. The influence of physicochemical properties of pond water, especially nutrient levels (nitrate, nitrite, and orthophosphate) on the occurrence of these cyanobacteria was also analyzed. As a pioneering study on freshwater HABs in aquaculture ponds in the country, results of the study can provide information to improve the knowledge in bloom occurrence and prediction, and to develop freshwater HAB prevention and control methods.     

Utilization of Industrial Wastes from Mining and Packaging Industries in Wood-Plastic Composites

The manufacturing industry produces a lot of different by-products and waste. In this research, the utilization of different industrial wastes as a part of wood-plastic composites was tested. Limestone waste and carton cutting waste were tested by replacing part of the reinforcing fibers of the composite with these materials. The materials were made with the extrusion process, and they were tested for their mechanical properties, water absorption and thickness swelling. The materials were also viewed with a scanning electron microscope. The results showed that both industrial wastes affected the properties of the composite. Mining waste in the composite improved the moisture properties, impact strength and hardness of the material. Carton cutting waste improved the impact strength remarkably.     

Effects of publication bias on conservation planning

Conservation planners need reliable information on spatial patterns of biodiversity. However, existing data sets are skewed because some ecosystems, taxa, and locations are underrepresented. We determined how many articles have been published in recent decades on the biodiversity of different countries and their constituent provinces. We searched the Web of Science catalogues Science Citation Index (SCI) and Social Science Citation Index (SSCI) for biodiversity-related articles published from 1993 to 2016 that included country and province names. We combined data on research publication frequency with other provincial-scale factors hypothesized to affect the likelihood of research activity (i.e., economic development, human presence, infrastructure, and remoteness). Areas that appeared understudied relative to the biodiversity expected based on site climate likely have been inaccessible to researchers for reasons, notably armed conflict. Geographic publication bias is of most concern in the most remote areas of sub-Saharan Africa and South America. Our provincial-scale model may help compensate for publication biases in conservation planning by revealing the spatial extent of research needs and the low cost of redoing this analysis annually.     

Population viability and harvest sustainability for Madagascar lemurs

Subsistence hunting presents a conservation challenge by which biodiversity preservation must be balanced with safeguarding of human livelihoods. Globally, subsistence hunting threatens primate populations, including Madagascar's endemic lemurs. We used population viability analysis to assess the sustainability of lemur hunting in Makira Natural Park, Madagascar. We identified trends in seasonal hunting of 11 Makira lemur species from household interview data, estimated local lemur densities in populations adjacent to focal villages via transect surveys, and quantified extinction vulnerability for these populations based on species-specific demographic parameters and empirically derived hunting rates. We compared stage-based Lefkovitch with periodic Leslie matrices to evaluate the impact of regional dispersal on persistence trajectories and explored the consequences of perturbations to the timing of peak hunting relative to the lemur birth pulse, under assumptions of density-dependent reproductive compensation. Lemur hunting peaked during the fruit-abundant wet season (March–June). Estimated local lemur densities were roughly inverse to body size across our study area. Life-history modeling indicated that hunting most severely threatened the species with the largest bodies (i.e., Hapalemur occidentalis, Avahi laniger, Daubentonia madagascariensis, and Indri indi), characterized by late-age reproductive onsets and long interbirth intervals. In model simulations, lemur dispersal within a regional metapopulation buffered extinction threats when a majority of local sites supported growth rates above the replacement level but drove regional extirpations when most local sites were overharvested. Hunt simulations were most detrimental when timed to overlap lemur births (a reality for D. madagascariensis and I. indri). In sum, Makira lemurs were overharvested. Regional extirpations, which may contribute to broad-scale extinctions, will be likely if current hunting rates persist. Cessation of anthropogenic lemur harvest is a conservation priority, and development programs are needed to help communities switch from wildlife consumption to domestic protein alternatives.     

Attenuation potentials of royal jelly against hydroxyurea-induced infertility through inhibiting oxidation and release of pro-inflammatory cytokines in male rats

Environmental Science and Pollution Research - Hydroxyurea (HDU), a class of antineoplastic drugs, has a powerful efficacy in the treatment of several types of malignancies. However, it has...     

PM10 and surface dust source characterization in Baguio City Central Business District (CBD), Philippines

Environmental Geochemistry and Health - This study measured both PM10 and surface dust concentrations at roadside in the Central Business District of Baguio City. A total of 66 PM10 filters and 25...     

Honeybees (<Emphasis Type="Italic">Apis mellifera</Emphasis>) as a biological barrier for contamination of honey by environmental toxic metals

The aim of the study was to investigate the transfer of toxic metals from honeybee workers (Apis mellifera L.) to bee honey in relation to the ecological state of the environment. The materials of the study consisted of samples of honeybee bodies and varietal honeys taken from the same apiary located in three areas: R1—urbanized (16), R2—ecologically clean (16) and R3—industrialized (15) of south-eastern Poland. The contents of 14 elements in all tested samples, including toxic metals (Cd, Pb, Hg, Al, Ni, Tl) as well as bioelements (K, Mg, Ca, Mn, Fe, Zn, Cu, Se), were analysed by the ICP-OES method with prior microwave mineralization. The concentrations of the majority of the studied elements, excluding aluminum and lead, were significantly higher in bee bodies than in honey samples (P?<?0.05). The pollution of bee bodies by toxic metals was dependent on the environmental cleanliness, and the most pollution was observed in the industrialized area. The bee body was the most effective barrier for Cd and Tl transfer to the honey, while the level of Ni was similar in both tested materials. The Al concentration was significantly higher in honey than bee bodies (14.81?±?24.69 and 6.51?±?5.83 mg kg^?1, respectively), which suggests the possibility of secondary contamination of honey. The greatest sensitivity to heavy metal pollution was observed in honeydew honey compared to nectar honeys (P?<?0.05). It was proved for the first time that bees work as biofilters for toxic metals and prevent honey contamination.     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.