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 Ͻ10,000 years ago, after the most recent glaciation, and that migration rates are strongly male biased. M AKING quantitative inferences on molecular data of models that more closely reflect the complexity of real processes, potentially allowing the estimation of more in complex demographic settings remains an ongoing challenge. Traditionally, inferences have been meaningful biological parameters. Many species have had a complex history that has made using summary statistics under simplified models (e.g., Fu and Chakraborty 1998). While sometimes useincluded a spatial expansion from a restricted range (e.g., an expansionary period following an ice age), with ful for qualitative comparisons, these simple models do not adequately reflect the complexity of processes that the establishment of new demes and the exchange of genes among those demes (Hewitt 2000; Ray et al. might affect molecular genetic diversity. Recent advances 2003;Excoffier 2004). Although these expansion proin maximum-likelihood and Bayesian approaches have cesses affect various aspects of molecular diversity differshown that it is possible to make full use of the data ently and can thus be described using a combination of gathered from population samples (e.g., Beaumont 1999; summary statistics, making joint estimates of expansion Beerli and Felsenstein 2001; Nielsen and Wakely parameters in an explicitly spatial setting would be diffi-2001; Wang and Whitlock 2003). Although these cult using either full-likelihood or more conventional methods have the potential to be accurate, the calculamethods. However, it may be possible to use an ABC tion of likelihoods under complex models can be probapproach to simultaneously infer spatial expansion palematic, necessitating the use of simplified demographic rameters in the context of an appropriate spatial model. models. A promising alternative approach is to compare Additionally, data from differently inherited molecular summary statistics that are calculated from observed markers can be used to investigate interesting biological data and related to the parameter(s) of interest, with phenomena such as differences in dispersal rates besummary statistics simulated under a model for which tween the sexes after a range expansion. the parameters are known (e.g., Fu an...

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.

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.ethnogenetics ͉ human evolution ͉ sex-bias dispersal G enetic analyses have supported the existence of sex-biased gene flow in various human populations (1-4). Several studies have shown that women could move among populations at higher rates than men (5-8), potentially explaining lower levels of local differentiation for mtDNA than Y chromosome markers (e.g., 9, 10), even though a recent analysis revealed similar levels of genetic structure at a broader scale (11). Different forms of social organization can impact patterns and levels of genetic diversity (12, 13), and sex differences in postmarital residence choice have been proposed to greatly affect isolation by distance patterns in humans (6). The patterns of gender-specific genetic markers, such as mitochondrial and Y chromosome diversity, were recently found to be deeply affected by postmarital residence choice in six populations of northern Thailand (14). Patrilocal populations, where men remain in their natal village and women move to their husband's village, showed lower levels of Y chromosome diversity than matrilocal populations; the reverse situation was observed for mtDNA diversity. Moreover, genetic distances were found to be lower for mtDNA between patrilocal populations than between matrilocal populations, a situation that was reversed for Y chromosome markers (14). Although these results strongly supported the view that sex-biased dispersal shaped patterns of diversity within and between populations, no attempt was made to quantify and compare the movement of males and females in the two types of societies. Such quantitation may yield additional insights into cult...

1. Accurate detection of individual animals is integral to the management of vulnerable wildlife species, but often difficult and costly to achieve for species that occur over wide or inaccessible areas or engage in cryptic behaviours. There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms.While manual evaluation of drone-acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers.Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194).2. We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone-acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods.3. From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in droneacquired imagery. A high probability of automated detection could be achieved efficiently using fixed-wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats.4. The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.

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