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Field sampling is biased against small-ranged species of high conservation value: a case study on the sphingid moths of East Africa
Authors:Jan Beck  Hitoshi Takano  Liliana Ballesteros-Mejia  Ian J Kitching  Christy M McCain
Institution:1.University of Colorado, Museum of Natural History,Boulder,USA;2.Department of Life Sciences,Natural History Museum,London,UK;3.Sorbonne Universités, Muséum national d’Histoire naturelle,Paris,France;4.The African Natural History Research Trust,Leominster,UK;5.Department of Zoology,University of Oxford,Oxford,UK;6.Department of Ecology and Evolutionary Biology,University of Colorado,Boulder,USA
Abstract:The range size of species co-occurring in local assemblages is a pivotal variable in assessments of a site’s conservation value. Assemblages featuring many small-ranged species are given more priority than assemblages consisting mainly of wide-ranging species. However, the assembly of relevant information can be challenging and local range size distributions of tropical invertebrates are rarely available for conservation planning. We present such data for sphingid moths in East Africa, a highly diverse region of high conservation value. We compare geographic range size distributions based on field samples with predictions from modelled range map data. Using this system as a case study, we provide evidence for a systematic sampling bias when inferring average local range sizes from field data. Unseen species (i.e., species present but missed in local sampling) are often those with small ranges (hence, of high conservation value). Using an elevational gradient, we illustrate how this bias can lead to false, counterintuitive assessments of environmental effects on local range size distributions. Furthermore, with particular reference to sphingid moths in the study region, we show that current protected areas appear unrelated to the spatial distribution of species richness or average geographic range sizes at a local scale. We discuss the need to treat field sampled data with caution and in concert with other data sources such as probabilistic models.
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