Science Enabled by Specimen Data

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…

Inman, R., Franklin, J., Esque, T., & Nussear, K. (2018). Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models. Quaternary Science Reviews, 198, 115–125. doi:10.1016/j.quascirev.2018.08.015 https://doi.org/10.1016/j.quascirev.2018.08.015

The ability to infer paleo-distributions with limited knowledge of absence makes species distribution modeling (SDM) a useful tool for exploring paleobiogeographic questions. Spatial sampling bias is a known issue when modeling extant species. Here we quantify the spatial sampling bias in a North Am…

Guevara, L., & Sánchez-Cordero, V. (2018). New records of a critically endangered shrew from Mexican cloud forests (Soricidae, Cryptotis nelsoni) and prospects for future field research. Biodiversity Data Journal, 6. doi:10.3897/bdj.6.e26667 https://doi.org/10.3897/BDJ.6.e26667

The Nelson´s small-eared shrew, Cryptotisnelsoni (Merriam, 1895), is a critically endangered species, endemic to cloud forests in Los Tuxtlas, a mountain range along the Gulf of Mexico coast. This species is only known from the type locality and its surroundings. Here we present new records that ext…