A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito

Understanding past dispersal and breeding events can provide insight into ecology and evolution, and can help inform strategies for conservation and the control of pest species. However, parent-offspring dispersal can be difficult to investigate in rare species and in small pest species such as mosquitoes. Here we develop a methodology for estimating parent-offspring dispersal from the spatial distribution of close kin, using pairwise kinship estimates derived from genome-wide single nucleotide polymorphisms (SNPs). SNPs were scored in 162 Aedes aegypti (yellow fever mosquito) collected from eight close-set, high-rise apartment buildings in an area of Malaysia with high dengue incidence. We used the SNPs to reconstruct kinship groups across three orders of kinship. We transformed the geographical distances between all kin pairs within each kinship category into axial standard deviations of these distances, then decomposed these into components representing past dispersal events. From these components, we isolated the axial standard deviation of parent-offspring dispersal, and estimated neighbourhood area (129 m), median parent-offspring dispersal distance (75 m), and oviposition dispersal radius within a gonotrophic cycle (36 m). We also analysed genetic structure using distance-based redundancy analysis and linear regression, finding isolation by distance both within and between buildings and estimating neighbourhood size at 268 individuals. These findings indicate the scale required to suppress local outbreaks of arboviral disease and to target releases of modified mosquitoes for mosquito and disease control. Our methodology is readily implementable for studies of other species, including pests and species of conservation significance

Unbiased population heterozygosity estimates from genome‐wide sequence data

Abstract Heterozygosity is a metric of genetic variability frequently used to inform the management of threatened taxa. Estimating observed and expected heterozygosities from genome‐wide sequence data has become increasingly common, and these estimates are often derived directly from genotypes at single nucleotide polymorphism (SNP) markers. While many SNP markers can provide precise estimates of genetic processes, the results of ‘downstream’ analysis with these markers may depend heavily on ‘upstream’ filtering decisions. Here we explore the downstream consequences of sample size, rare allele filtering, missing data thresholds and known population structure on estimates of observed and expected heterozygosity using two reduced‐representation sequencing datasets, one from the mosquito Aedes aegypti (ddRADseq) and the other from a threatened grasshopper, Keyacris scurra (DArTseq). We show that estimates based on polymorphic markers only (i.e. SNP heterozygosity) are always biased by global sample size (N), with smaller N producing larger estimates. By contrast, results are unbiased by sample size when calculations consider monomorphic as well as polymorphic sequence information (i.e. genome‐wide or autosomal heterozygosity). SNP heterozygosity is also biased when differentiated populations are analysed together while autosomal heterozygosity remains unbiased. We also show that when nucleotide sites with missing genotypes are included, observed and expected heterozygosity estimates diverge in proportion to the amount of missing data permitted at each site. We make three recommendations for estimating genome‐wide heterozygosity: (a) autosomal heterozygosity should be reported instead of (or in addition to) SNP heterozygosity; (b) sites with any missing data should be omitted and (c) populations should be analysed in independent runs. This should facilitate comparisons within and across studies and between observed and expected measures of heterozygosity

Mobility as a service (MaaS): the importance of transportation psychology

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Mobility-as-a-Service (MaaS) is based on the notion that consumers and transport providers access a centralized platform for the planning, payment, and management of trips and combines multiple modes of transportation designed to increase the efficiency of the system. MaaS offers substantial societal benefits, including the reduction of emissions, traffic congestion, road injuries, and the overall discomfort associated with travel, in addition to providing personalized transportation solutions. Since the delivery of these benefits hinges on the widespread adoption of MaaS platforms, we draw on consumer psychology for insight into the social and cognitive psychological factors that may hamper the adoption of MaaS, and their influence on consumer choices and perceptions. More generally, this paper highlights that transportation is a fertile context for consumer psychology research