Science Enabled by Specimen Data

Wint, G. R. W., T. Balenghien, E. Berriatua, M. Braks, C. Marsboom, J. Medlock, F. Schaffner, et al. 2023. VectorNet: collaborative mapping of arthropod disease vectors in Europe and surrounding areas since 2010. Eurosurveillance 28. https://doi.org/10.2807/1560-7917.es.2023.28.26.2200666

Background Arthropod vectors such as ticks, mosquitoes, sandflies and biting midges are of public and veterinary health significance because of the pathogens they can transmit. Understanding their distributions is a key means of assessing risk. VectorNet maps their distribution in the EU and surrounding areas. Aim We aim to describe the methodology underlying VectorNet maps, encourage standardisation and evaluate output. Method s: Vector distribution and surveillance activity data have been collected since 2010 from a combination of literature searches, field-survey data by entomologist volunteers via a network facilitated for each participating country and expert validation. Data were collated by VectorNet members and extensively validated during data entry and mapping processes. Results As of 2021, the VectorNet archive consisted of ca 475,000 records relating to > 330 species. Maps for 42 species are routinely produced online at subnational administrative unit resolution. On VectorNet maps, there are relatively few areas where surveillance has been recorded but there are no distribution data. Comparison with other continental databases, namely the Global Biodiversity Information Facility and VectorBase show that VectorNet has 5–10 times as many records overall, although three species are better represented in the other databases. In addition, VectorNet maps show where species are absent. VectorNet’s impact as assessed by citations (ca 60 per year) and web statistics (58,000 views) is substantial and its maps are widely used as reference material by professionals and the public. Conclusion VectorNet maps are the pre-eminent source of rigorously validated arthropod vector maps for Europe and its surrounding areas.

Li, D., Z. Li, Z. Liu, Y. Yang, A. G. Khoso, L. Wang, and D. Liu. 2022. Climate change simulations revealed potentially drastic shifts in insect community structure and crop yields in China’s farmland. Journal of Pest Science. https://doi.org/10.1007/s10340-022-01479-3

Climate change will cause drastic fluctuations in agricultural ecosystems, which in turn may affect global food security. We used ecological niche modeling to predict the potential distribution for four cereal aphids (i.e., Sitobion avenae, Rhopalosiphum padi, Schizaphis graminum, and Diurphis noxia…

Kolanowska, M. 2021. The future of a montane orchid species and the impact of climate change on the distribution of its pollinators and magnet species. Global Ecology and Conservation 32: e01939. https://doi.org/10.1016/j.gecco.2021.e01939

The aim of this study was to evaluate the impact of global warming on suitable niches of montane orchid, Traunsteinera globosa, using ecological niche modelling approach. Additionally, the effect of various climate change scenarios on future changes in the distribution and overlap of the orchid magn…

Liu, X., T. M. Blackburn, T. Song, X. Wang, C. Huang, and Y. Li. 2020. Animal invaders threaten protected areas worldwide. Nature Communications 11. https://doi.org/10.1038/s41467-020-16719-2

Protected areas are the cornerstone of biodiversity conservation. However, alien species invasion is an increasing threat to biodiversity, and the extent to which protected areas worldwide are resistant to incursions of alien species remains poorly understood. Here, we investigate establishment by 8…

Piel, W. H. 2018. The global latitudinal diversity gradient pattern in spiders. Journal of Biogeography 45: 1896–1904. https://doi.org/10.1111/jbi.13387

Aim: The aim of this study was to test the hypothesis that the global latitudinal diversity gradient pattern in spiders is pear‐shaped, with maximum species diversity shifted south of the Equator, rather than egg‐shaped, centred on the equator, this study infers the gradient using two large datasets…