The evolutionary history of species is a dynamic process as they modify, expand, and contract their spatial distributions over time. Range expansions (REs) occur through a series of founder events that are followed by migration among neighboring demes. The process usually results in structured metapopulations and leaves a distinct signature in the genetic variability of species. Explicitly modeling the consequences of complex demographic events such as REs is computationally very intensive. Here we propose an an alternative approach that requires less computational effort than a comprehensive RE model, but that can recover the demography of species undergoing a RE, by combining spatially explicit modelling with simplified but realistic metapopulation models. We examine the demographic and colonization history of Carcharhinus melanopterus, an abundant reef-associated shark, as a test case. We first used a population genomics approach to statistically confirm the occurrence of a RE in C. melanopterus, and identify its origin in the Indo-Australian Archipelago. Spatial genetic modelling identified two waves of stepping-stone colonization: an eastward wave moving through the Pacific and a westward one moving through the Indian Ocean. We show that metapopulation models best describe the demographic history of this species and that not accounting for this may lead to incorrectly interpreting the observed genetic variation as signals of widespread population bottlenecks. Our study highlights insights that can be gained about demography by coupling metapopulation models with spatial modeling and underscores the need for cautious interpretation of population genetic data when advancing conservation priorities.Electronic supplementary material The online version of this article (https://doi.org/10.1038/s41437-018-0164-0) contains supplementary material, which is available to authorized users.

Significance Only an estimated 1 to 10% of Earth’s species have been formally described. This discrepancy between the number of species with a formal taxonomic description and actual number of species (i.e., the Linnean shortfall) hampers research across the biological sciences. To explore whether the Linnean shortfall results from poor taxonomic practice or not enough taxonomic effort, we applied machine-learning techniques to build a predictive model to identify named species that are likely to contain hidden diversity. Results indicate that small-bodied species with large, climatically variable ranges are most likely to contain hidden species. These attributes generally match those identified in the taxonomic literature, indicating that the Linnean shortfall is caused by societal underinvestment in taxonomy rather than poor taxonomic practice.

Table 1. Species used in analysis. For each species, the scientific name, type of organism, type of data, number of sequences, and reference of original publication is shown. SpeciesBroad Taxon Type of Data # sequences Original publication Bryopsis sp. Green Algae cpDNA 66 Krellwitz et al. (2001) Gracilaria tikvahiae Red Algae cpDNA 20 Gurgel et al.(2004) Xerula furfuracea Fungi nuDNA 41 Yang et al.(2009) & Petersen and Hughes (2010) & Hao et al.(2016) Sphagnum bartlettianum Bryophyta cpDNA + nuDNA 12 Shaw et al.(2005) Acer rubrum Angiosperm cpDNA 38 McLachlan et al.(2005) Apios americana Angiosperm nuDNA 18 Joly & Bruneau (2004) Dicerandra spp Angiosperm cpDNA 30 Oliveira et al.(2007) Fagus grandifolia Angiosperm cpDNA 23 McLachlan et al.(2005) Liquidambar styraciflua Angiosperm cpDNA 109 Morris et al.(2008) Prunus spp Angiosperm cpDNA 226 Shaw & Small (2005) Tilia americana Angiosperm cpDNA 297 McCarthy and Mason-Gamer (2016) Trillium cuneatum Angiosperm cpDNA 281 Gonzales et al.(2008) Uniola paniculata Angiosperm cpDNA 131 Hodel & Gonzales (2013) Bugula neritina Bryozoa mtDNA 30 McGovern & Hellberg (2003) Daphnia obtusa Crustacean mtDNA 36 Penton et al.(2004) Emerita talpoida Crustacean mtDNA 4 Tam et al.(1996) Farfantepenaeus aztecus Crustacean mtDNA 76 McMillen-Jackson and Bert (2003) Litopenaeus setiferus Crustacean mtDNA 92 McMillen-Jackson and Bert (2003) & Maggioni et al. (2001) &Vazquez-Bader et al.(2004) & Bremer et al.(2010) Pagarus longicarpus Crustacean mtDNA 67 Young et al.(2002) Pagarus pollicaris Crustacean mtDNA 13 Young et al.(2002) Busycon sinistrum Gastropod mtDNA 31 Wise et al.(2004) Lampsilis altilis Mollusk mtDNA 5 Roe et al.(2001) Lampsilis australis Mollusk mtDNA 5 Roe et al.(2001) Lampsilis ovata Mollusk mtDNA 2 Roe et al.(2001) & Campbell et al.(2005) Lampsilis perovalis Mollusk mtDNA 5 Roe et al.(2001) Lampsilis teres Mollusk mtDNA 2 Roe et al.(2001) & Lydeard et al.(2000) Spisula solidissima Mollusk mtDNA 52 Hare and Weinberg (2005) Ambystoma tigrinum Amphibian mtDNA 56 Church et al.(2003) Desmognathus wrightii Amphibian mtDNA 29 Crespi et al.(2003) Eumeces fasciatus Amphibian mtDNA 82 Howes et al.(2006) Eurycea bislineata Amphibian mtDNA 56 Kozak et al.(2006) Eurycea cirrigera Amphibian mtDNA 251 Kozak et al.(2006) Eurycea junaluska Amphibian mtDNA 6 Kozak et al.(2006) Eurycea multiplicata Amphibian mtDNA 46 Bonett & Chippindale (2004) Eurycea tymerensis Amphibian mtDNA 16 Bonett & Chippindale (2004) Eurycea wilderae Amphibian mtDNA 129 Kozak et al.(2006)

The patterns of genetic and morphological diversity of a widespread species can be influenced by environmental heterogeneity and the degree of connectivity across its geographic distribution. Here, we studied Quercus havardii Rydb., a uniquely adapted desert oak endemic to the Southwest region of the United States, using genetic, morphometric, and environmental datasets over various geographic scales to quantify differentiation and understand forces influencing population divergence. First, we quantified variation by analyzing 10 eastern and 13 western populations from the disjunct distribution of Q. havardii using 11 microsatellite loci, 17 morphological variables, and 19 bioclimatic variables. We then used regressions to examine local and regional correlations of climate with genetic variation. We found strong genetic, morphological and environmental differences corresponding with the large-scale disjunction of populations. Additionally, western populations had higher genetic diversity and lower relatedness than eastern populations. Levels of genetic variation in the eastern populations were found to be primarily associated with precipitation seasonality, while levels of genetic variation in western populations were associated with lower daily temperature fluctuations and higher winter precipitation. Finally, we found little to no observed environmental niche overlap between regions. Our results suggest that eastern and western populations likely represent two distinct taxonomic entities, each associated with a unique set of climatic variables potentially influencing local patterns of diversity.