Uses of GBIF in scientific research

Peer-reviewed research citing GBIF as a data source, with at least one author from Nepal.
For all researches, please visit our "Peer-reviewed publications" page.

List of publications

  • Kandel K, Huettmann F, Suwal M, Ram Regmi G, Nijman V, Nekaris K et al. (2015)

    Rapid multi-nation distribution assessment of a charismatic conservation species using open access ensemble model GIS predictions: Red panda (Ailurus fulgens) in the Hindu-Kush Himalaya region

    Biological Conservation 181 150-161.

    The red panda (Ailurus fulgens) is a globally threatened species living in the multi-national Hindu-Kush Himalaya (HKH) region. It has a declining population trend due to anthropogenic pressures. Human-driven climate change is expected to have substantial impacts. However, quantitative and transparent information on the ecological niche (potential as well as realized) of this species across the vast and complex eight nations of the HKH region is lacking. Such baseline information is not only crucial for identifying new populations but also for restoring locally-extinct populations, for understanding its bio-geographical evolution, as well as for prioritizing regions and an efficient management. First we compiled, and made publicly available through an institutional repository (dSPACE), the best known ‘presence only’ red panda dataset with ISO compliant metadata. This was done through the International Centre for Integrated Mountain Development (ICIMOD.org) data-platform to the Global Biodiversity Information Facility (GBIF.org). We used data mining and machine learning algorithms such as high-performance commercial Classification and Regression Trees, Random Forest, TreeNet, and Multivariate Adaptive Regression Splines implementations. We averaged all these Geographic Information System (GIS) models for the first produced ensemble model for this species in the HKH region. Our predictive model is the first of its kind and allows to assess the red panda distribution based on empirical open access data, latest methods and the major signals and drivers of the ecological niche. It allows to assess and fine-tune earlier habitat area estimates. Our models promote ‘best professional practices’. It can readily be used by the red panda Recovery Team, the red panda Action Plan, etc. because they are robust, transparent, publicly available, fit for use, and have a good accuracy, as judged by several independent assessment metrics (Receiver Operating Characteristics (ROC-AUC) curves, expert opinion, assessed by known absence regions, 95% confidence intervals and new field data).

    Keywords: Ensemble model GIS prediction, Hindu-Kush Himalaya (HKH), Machine learning, Open access GBIF data, Red panda (Ailurus fulgens)


  • Mainali K, Warren D, Dhileepan K, McConnachie A, Strathie L, Hassan G et al. (2015)

    Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling.

    Global change biology 21(12) 4464-4480.

    Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our "best" model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

    Keywords: AUC, Parthenium hysterophorus, boosted regression trees, generalized additive models, generalized linear models, invasive species, model evaluation, nonequilibrium distribution, random forests, species distribution modeling


  • Rajbhandary S, Hughes M, Phutthai T, Thomas D, Shrestha K (2011)

    Asian Begonia: out of Africa via the Himalayas?

    Gardens' Bulletin Singapore 63(1 & 2) 277-286.

    The large genus Begonia began to diverge in Africa during the Oligocene. The current hotspot of diversity for the genus in China and Southeast Asia must therefore be the result of an eastward dispersal or migration across the Asian continent. To investigate the role of the Himalayas as a mesic corridor facilitating this migration, we constructed a time- calibrated molecular phylogeny using ITS sequence data. Himalayan species of Begonia were found to fall into two groups. The first is an unresolved grade of tuberous, deciduous species of unknown geographic origin, with evidence of endemic radiations in the Himalayan region beginning c. 7.4 Ma, coinciding with the onset of the Asian monsoon. The second is a group of evergreen rhizomatous species with a probable origin in China, which immigrated to the Himalayan region c. 5.1 Ma, coinciding with an intensification of the monsoon. The hypothesis of the Himalayas being a mesic migration route during the colonisation of Asia is not refuted, but further data is needed.

    Keywords: Begonia, China, Himalayas, biogeography, molecular phylogeny, southeast Asia