Indonesian Institute of Sciences

About: Indonesian Institute of Sciences is a facility organization based out in Jakarta, Indonesia. It is known for research contribution in the topics: Population & Biology. The organization has 4795 authors who have published 10544 publications receiving 76990 citations. The organization is also known as: Indonesian Institute of Sciences Cibinong, Indonesia.

Taxonomy and phylogeny of Leptopilina species (Hymenoptera: Cynipoidea: Figitidae) attacking frugivorous drosophilid flies in Japan, with description of three new species

Abstract: Despite the intensive use of the Leptopilina genus and its drosophilid hosts as model systems in the study of host–parasitoid interactions, the diversity and distribution of the species occurring in the Asian region remain elusive. Here we report the phylogeny of Japanese Leptopilina species attacking frugivorous drosophilid flies, based on COI, ITS1 and ITS2 sequences. Consistent with molecular data, hybridization experiments and morphological examination, five species were recorded in Japan: Leptopilina heterotoma, L. victoriae and three new species, two occurring in the Ryukyu archipelago, L. ryukyuensis and L. pacifica, and another species, L. japonica, distributed in Honshu and Hokkaido. Leptopilina japonica is further divided into two subspecies, L. j. japonica occurring in Japan, and L. j. formosana occurring in Taiwan. According to these results, we discuss the evolution, speciation and colonization history of Japanese Leptopilina species.

Enhancing coastal livelihoods in Indonesia: an evaluation of recent initiatives on gender, women and sustainable livelihoods in small-scale fisheries

Abstract: In recent decades, there have been considerable efforts to enhance, diversify, or implement alternative livelihood activities in marginalized coastal communities, to ease reliance on deteriorating coastal resources, reduce poverty and improve well-being outcomes. To date, gender has been notably absent from the literature on small-scale fisheries and associated livelihood improvement programs, despite increasing evidence of the importance of gender equality and women’s empowerment in achieving such outcomes in other contexts. In this paper, drawing from an evaluation of the effectiveness of 20 livelihood development projects implemented in coastal communities in Indonesia since 1998, we report on how gender was considered in these projects. We assessed whether and how gender was included in project rationales, and how men and women were included in project activities. We found that, despite the women being reached by many project activities, particularly efforts to increase women’s productive capacity through training and group-based livelihoods enterprises, 40% of the projects had no discernible gender approach and only two of the 20 projects (10%) applied a gender transformative approach that sought to challenge local gender norms and gender relations and empower women beneficiaries. Our assessment suggests the need for greater understanding of the role of gender in reducing poverty and increasing well-being outcomes in coastal communities. Lessons from comparable agricultural settings suggest that this may be facilitated by locally situated gender social relations analysis, integration of gender throughout livelihood improvement project cycles, gendered capacity building activities and shared learning from the evaluation of the gendered outcomes of project activities.

Deep Structured Convolutional Neural Network for Tomato Diseases Detection

Abstract: Plant diseases outbreaks can cause significant threat to food security. Early detection of the diseases using machine learning could avoid such disaster. Currently, deep learning, which is a recent technology in machine learning, gained much popularity for object recognition tasks. Convolutional neural network (CNN) is one major techniques for object identification in deep learning. In this paper, we evaluate the effect of different depth of CNN architectures on the detection accuracies of the plant diseases detection. Various CNN architectures with different depth are investigated. They are simple CNN baseline (with two layer of convolutional layers), AlexNet (with five convolutional layers), and VGGNet (with 13 convolutional layers). We also evaluate GoogleNet architectures. Unlike previously mentioned architectures, GoogleNet use convolutional layers with various resolutions to be concantenated with each other, emphasizing the effect on not only the deep architecture but also a wide one. The experimental results suggest that CNN with deeper architecture, i.e. VGGNet, outperforms others, indicating that having deeper architectures may be more benefit for this task.

Cost-effectiveness of plant and animal biodiversity indicators in tropical forest and agroforest habitats

Abstract: Summary 1. Biodiversity data are needed for conservation and management of tropical habitats, but the high diversity of these ecosystems makes comprehensive surveys prohibitively expensive and indicator taxa reflecting the biodiversity patterns of other taxa are frequently used. Few studies have produced the necessary comprehensive data sets to assess the quality of the indicator groups, however, and only one previous study has considered the monetary costs involved in sampling them. 2. We surveyed four plant groups (herbs, liverworts, trees, lianas) and eight animal groups (ants, canopy and dung beetles, birds, butterflies, bees, wasps and the parasitoids of the latter two) in 15 plots of 50 · 50 m 2 each, representing undisturbed rainforest and two types of cacao agroforest in Sulawesi, Indonesia. We calculated three biodiversity measures (a and b diversity; percentage of species indicative of habitat conditions), built simple and multiple regression models among species groups (single groups, combinations of 2–11 groups, averaged relative diversity of all 12 groups), and related these to three measures of survey cost (absolute costs and two approaches correcting for different sampling intensities). 3. Determination coefficients (R 2 values) of diversity patterns between single study groups were generally low (<0AE25), while the consideration of several study groups increased R 2 values to up to 0AE8 for combinations of four groups, and to almost 1AE0 for combinations of 11 groups. Survey costs varied 10-fold between study groups, but their cost-effectiveness (indicator potential versus monetary cost) varied strongly depending on the biodiversity aspect taken into account (a or b diversity, single or multiple groups, etc.). 4. Synthesis and applications. We found that increasing the number of taxa resulted in best overall biodiversity indication. We thus propose that the most cost-efficient approach to general tropical biodiversity inventories is to increase taxonomic coverage by selecting taxa with the lowest survey costs.