Anderson, Robert P. “Harnessing the world’s biodiversity data: promise and peril in ecological niche modeling of species distributions.” Annals of the New York Academy of Sciences 1260.1 (2012): 66-80.
DOI: 10.1111/j.1749-6632.2011.06440.x
The advances in stores of biological and environmental data (presence-only data) from museums facilitate species niches and geographic distribution modeling, which offers key insights for conservation biology, management of invasive species, zoonotic human disease, and other pressing environmental problems. However, the full utility of niche modeling remains under-realized, which mainly lies in both the incomplete availability of the occurrence data (1, incorrect taxonomic identifications; 2, lacking or inadequate databasing and georeferences; 3, effects of sampling bias across gepgraphy) and the nascent nature of the field, with few researchers well trained conceptually and methodologically (i.e. 4, selection of the study region; and 5, model evaluation to identify optimal model complexity). The authors highlighted that the critical applications of museum data via SDM represent an opportunity for museums to contribute information and solutions to key societal issues, as well as a compelling justification for investment in the taxonomic studies of biodiversity. The selection of the study region for model calibration represents a topic of great importance. Studies show that environmental data from regions that may hold suitable conditions but in which the species is absent for other reasons should not be included in background samples. To be specific, the absence may be due to dispersal barriers or because biotic interactions. Although limited numbers of studies take into account paramount principles of study-region selection and extrapolation in environmental space, they have been stated clearly in literature. Finally, researchers should elaborate good performance for SDM before interpreting and using them for applications, including whether the model predicts independent data well and whether it has the ability to predict across time and/or space. The author claimed for a necessity to produce a much larger number of scientists capable of building and applying high-quality SDM, as well as a broad community able to acknowledge their quality and utility. I highly agree with the author that SDM is on its way to thrive and making practical contributions for biodiversity studies. One of the first-hand experiences I have during this semester is that there are still barriers between researchers from different traditional “disciplines”, both in understanding of theory or technology. Epitomizing the interdisciplinary nature of the field is critical to promote further development of SDM and biodiversity informatics.