Approximate Bayesian computation (ABC) is a highly flexible technique that allows the estimation of parameters under demographic models that are too complex to be handled by full-likelihood methods. We assess the utility of this method to estimate the parameters of range expansion in a two-dimensional stepping-stone model, using samples from either a single deme or multiple demes. A minor modification to the ABC procedure is introduced, which leads to an improvement in the accuracy of estimation. The method is then used to estimate the expansion time and migration rates for five natural common vole populations in Switzerland typed for a sex-linked marker and a nuclear marker. Estimates based on both markers suggest that expansion occurred <10,000 years ago, after the most recent glaciation, and that migration rates are strongly male biased.

Human social organization can deeply affect levels of genetic diversity. This fact implies that genetic information can be used to study social structures, which is the basis of ethnogenetics. Recently, methods have been developed to extract this information from genetic data gathered from subdivided populations that have gone through recent spatial expansions, which is typical of most human populations. Here, we perform a Bayesian analysis of mitochondrial and Y chromosome diversity in three matrilocal and three patrilocal groups from northern Thailand to infer the number of males and females arriving in these populations each generation and to estimate the age of their range expansion. We find that the number of male immigrants is 8 times smaller in patrilocal populations than in matrilocal populations, whereas women move 2.5 times more in patrilocal populations than in matrilocal populations. In addition to providing genetic quantification of sex-specific dispersal rates in human populations, we show that although men and women are exchanged at a similar rate between matrilocal populations, there are far fewer men than women moving into patrilocal populations. This finding is compatible with the hypothesis that men are strictly controlling male immigration and promoting female immigration in patrilocal populations and that immigration is much less regulated in matrilocal populations.

Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species.

Rosenberg et al. (1) described the genetic structure of 52 human populations from five continents studied at 377 short tandem repeat (STR) loci. This high-resolution study demonstrated that, using multilocus information only, the individuals of these populations could be subdivided into five stable genetic clusters that correspond to five major geographic regions: sub-Saharan Africa, the Americas, Oceania, EastAsia, and Eurasia (Europe, the Middle East, Central and South Asia). The study also partitioned the total genetic variance into components based upon differences between individuals within populations, between populations within regions, and between these five regions [table 1 in (1)]. Surprisingly, Rosenberg et al. (1) found very small differences between regions— only 4 to 5.7% of total diversity depending on regional sampling intensity, which is roughly half that of previous estimates inferred from molecular markers (2–4) (Table 1). Although the authors attributed that odd result to differences in sampling coverage among studies, we note that they did not base their analysis on the specific stepwise mutation model prevailing at STR loci, which is characterized by recurrent mutations. Ignoring the possibility that the same allelic type found in different individuals or populations may be derived from different evolutionary processes is known to lead to biased estimates of genetic structure (5–7)