Science Enabled by Specimen Data

Orr, M. C., Hughes, A. C., Chesters, D., Pickering, J., Zhu, C.-D., & Ascher, J. S. (2020). Global Patterns and Drivers of Bee Distribution. Current Biology. doi:10.1016/j.cub.2020.10.053 https://doi.org/10.1016/j.cub.2020.10.053

Insects are the focus of many recent studies suggesting population declines, but even invaluable pollination service providers such as bees lack a modern distributional synthesis. Here, we combine a uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence re…

Oegelund Nielsen, R., da Silva, R., Juergens, J., Staerk, J., Lindholm Sørensen, L., Jackson, J., … Conde, D. A. (2020). Standardized data to support conservation prioritization for sharks and batoids (Elasmobranchii). Data in Brief, 33, 106337. doi:10.1016/j.dib.2020.106337 https://doi.org/10.1016/j.dib.2020.106337

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Li, X., Li, B., Wang, G., Zhan, X., & Holyoak, M. (2020). Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term. MethodsX, 7, 101067. doi:10.1016/j.mex.2020.101067 https://doi.org/10.1016/j.mex.2020.101067

In multiple regression Y ~ β0 + β1X1 + β2X2 + β3X1 X2 + ɛ., the interaction term is quantified as the product of X1 and X2. We developed fractional-power interaction regression (FPIR), using βX1M X2N as the interaction term. The rationale of FPIR is that the slopes of Y-X1 regression along the X2 gr…

Freitas, T. M. da S., Stropp, J., Calegari, B. B., Calatayud, J., De Marco, P., Montag, L. F. de A., & Hortal, J. (2020). Quantifying shortfalls in the knowledge on Neotropical Auchenipteridae fishes. Fish and Fisheries. doi:10.1111/faf.12507 https://doi.org/10.1111/faf.12507

The Neotropics harbour the greatest diversity of freshwater fish on Earth. Despite recent advances in characterizing the fish fauna, the total number of species, distributional range, evolution and ecological traits remain uncertain. Thus, we quantify shortfalls in the knowledge of taxonomy (Linnean…

Kilpatrick, S. K., Gibbs, J., Mikulas, M. M., Spichiger, S.-E., Ostiguy, N., Biddinger, D. J., & Lopez-Uribe, M. M. (2020). An updated checklist of the bees (Hymenoptera, Apoidea, Anthophila) of Pennsylvania, United States of America. Journal of Hymenoptera Research, 77, 1–86. doi:10.3897/jhr.77.49622 https://doi.org/10.3897/jhr.77.49622

Checklists provide information about the species found in a defined region and serve as baselines for detecting species range expansions, contractions, or introductions. Bees are a diverse and important group of insect pollinators. Although some bee populations are declining, these patterns are diff…

Zigler, K., Niemiller, M., Stephen, C., Ayala, B., Milne, M., Gladstone, N., … Cressler, A. (2020). Biodiversity from caves and other sub-terranean habitats of Georgia, USA. Journal of Cave and Karst Studies, 82(2), 125–167. doi:10.4311/2019lsc0125 https://doi.org/10.4311/2019LSC0125

We provide an annotated checklist of species recorded from caves and other subterranean habitats in the state of Georgia, USA. We report 281 species (228 invertebrates and 53 vertebrates), including 51 troglobionts (cave-obligate species), from more than 150 sites (caves, springs, and wells). Endemi…

Freitas, T. M. S., Montag, L. F. A., De Marco, P., & Hortal, J. (2020). How reliable are species identifications in biodiversity big data? Evaluating the records of a neotropical fish family in online repositories. Systematics and Biodiversity, 1–11. doi:10.1080/14772000.2020.1730473 https://doi.org/10.1080/14772000.2020.1730473

The increase of free and open online biodiversity databases is of paramount importance for current research in ecology and evolution. However, little attention is paid to using updated taxonomy in these “biodiversity big data” repositories and the quality of their taxonomic information is often ques…

Daniel, J., Horrocks, J., & Umphrey, G. J. (2019). Efficient Modelling of Presence-Only Species Data via Local Background Sampling. Journal of Agricultural, Biological and Environmental Statistics. doi:10.1007/s13253-019-00380-4 https://doi.org/10.1007/s13253-019-00380-4

In species distribution modelling, records of species presence are often modelled as a realization of a spatial point process whose intensity is a function of environmental covariates. One way to fit a spatial point process model is to apply logistic regression to an artificial case–control sample c…

Ezray, B. D., Wham, D. C., Hill, C. E., & Hines, H. M. (2019). Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum. Proceedings of the Royal Society B: Biological Sciences, 286(1910), 20191501. doi:10.1098/rspb.2019.1501 https://doi.org/10.1098/rspb.2019.1501

Müllerian mimicry theory states that frequency-dependent selection should favour geographical convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes, each containing a predominant phenotype. Outside a few exam…

Pascoe, E. L., Marcantonio, M., Caminade, C., & Foley, J. E. (2019). Modeling Potential Habitat for Amblyomma Tick Species in California. Insects, 10(7), 201. doi:10.3390/insects10070201 https://doi.org/10.3390/insects10070201

The Amblyomma genus of ticks comprises species that are aggressive human biters and vectors of pathogens. Numerous species in the genus are undergoing rapid range expansion. Amblyomma ticks have occasionally been introduced into California, but as yet, no established populations have been reported i…